Abstracts of Oral Presentation Papers in Program as of 8/31/00
Conference Location Will Alternate Each Year
Internet Web Posting
Program changes and workshop announcements for this conference will be posted on the Internet World Wide Web at:
http://home.att.net/~drummond/
where abstracts of all papers scheduled for oral presentation for this conference will be posted and updated as changes occur.
Link to Short Course Description
The abstracts were compiled and this page was prepared by Dr. Neil J. Gordon,
Defence Evaluation and Research Agency Malvern
Closely spaced small object resolution and extended object imaging: detecting targets that obscure the background, V. M. Ginzburg, Consultant [4048-01]
In the first part of this paper it is shown that closely spaced small objects, by non-traditional Super-Scanning Locator (SSL), can be resolved with resolution better that by the Rayleigh criterion. Furthermore, one can create the real images (tomograms) of extended objects. A short description of theoretical and experimental research to develop a SSL with antennas performing beam scanning during both the emission and reception of pulses is presented. The reflected signals are received within discrete "visibility" layers (VLs) formed due to beam scanning during pulses radiation and reception. SSL has a number of advantages in comparison with the conventional locator. The known distribution of VLs in space allows one: to create adaptive systems ensuring reception of the required information with the minimum energy expenses; to attain "super-resolution" of objects (at distances smaller, than required by the Rayleigh criterion). An ultrasonic version of the SSL, working in air, at frequency range by bats and dolphin usage, is described. The experimental results totally confirm the theoretical predictions.
In the second part of the paper, the results of theoretical and experimental research to develop the methods and means for detection, observing and 3D images of static and dynamic objects disposed undersea, or inside or behind optically opaque solid media, are presented. The methods for undersea detection and measurements are based on optical refraction, diffraction, and the Talbot effects on the water surface disturbed by ultrasound waves reflected or passed by the object.
Use of multifractals to detect anomalous propagation (AP) in weather radar, D. Charalampidis, T. Kasparis, M. Georgiopoulos, Univ. of Central Florida [4048-02]
NO ABSTRACT PROVIDED
Tactical mid-infrared background suppression in heavy clutter environments, J. B. Montgomery, Mercer Engineering Research Ctr.; R. B. Sanderson, Air Force Research Lab.; F. O. Baxley, Veridian Inc. [4048-03]
NO ABSTRACT PROVIDED
Constrained subpixel target detection for hyperspectral imagery, C. Chang, D. Heinz, Univ. of Maryland/Baltimore County [4048-04]
NO ABSTRACT PROVIDED
Optimal point target detection using adaptive auto regressive background prediction, B. S. Denney, R. J. P. de Figueiredo, Neural Computing Systems [4048-05]
In this paper, we use an adaptive AR (Auto regressive) model to optimally filter background texture from images. The filter maps background texture into minimum variance locally white noise. An additive point target signal, however, is unaffected by the filter thereby effectively maximizing the signal to noise/clutter ratio. Thus, filter output thresholding can detect anomalous pixels in the images. Additionally the paper introduces a false alarm rejection scheme based on the intersection of a Four Quadrant AR (Quad-AR) filter. The paper addresses the implicit background assumptions in this approach and other small target detection schemes. An example application of the filter to infrared images of missiles immersed in intense sea glint is presented. The AR filter performance is compared to a median filter performance. It is shown that for the infrared sub-pixel missile over sea problem, the Quad-AR approach is substantially better than previous approaches.
Adaptive threshold-based spatio-temporal filtering techniques for detection of small targets, V. Ronda, W. L. New, M. H. Tan, M. H. Er, Nanyang Technological Univ. (Singapore) [4048-06]
NO ABSTRACT PROVIDED
Vision-based superresolution for recognizing small targets, R. W. Thomas, S. C. Gustafson, Air Force Institute of Technology [4048-69]
Small target images are typically represented by a small number of pixels (perhaps 100 or fewer). For a small number of pixels certain vision-based image processing techniques that implement super-resolution are feasible that would not be feasible for a large number of pixels. Among the most realistic are radial basis function techniques that center basis functions (such as Gaussian) of the same width on all pixels and adjust their amplitudes so that the sum of the basis functions integrated over each pixel is its gray value. In this technique the only undetermined parameter (once the basis function form is selected) is the basis function width. In this paper several methods, including methods based the condition number of the matrix that is inverted to find the basis function amplitudes, are investigated for choosing this parameter, and results are presented for sample small targets using easily-interpreted visualization techniques.
Selection of a clutter rejection algorithm for real-time target detection from an airborne platform, C. I. Hilliard, Ball Aerospace & Technologies Corp. [4048-08]
Clutter rejection is often an essential task in applications involving the detection and identification of small targets, making the choice of a clutter rejection algorithm extremely important if such a system is to perform as desired. Many different clutter rejection algorithms have been developed by various groups seeking to address this problem; however, as the performances of the algorithms are often very scenario dependent, selecting an appropriate algorithm for a given application usually requires thorough testing and performance analysis. This paper describes the methodology and results of a study done on clutter rejection algorithms for a system involving a staring IR camera mounted on an airborne platform. The purpose of this system is to detect the use of ordnance on a battlefield and then determine what type of ordnance was used. The clutter rejection algorithm needed to be real-time and possible to implement in hardware. The algorithms chosen for testing included 17 spatial filters and 4 temporal filters, along with two different types of thresholding (spatially fixed and spatially adaptive). Appropriate datasets for testing were created using a combination of real ordnance data taken by the IR camera, and clutter backgrounds from Modis Airborne Simulator. Several different metrics were chosen to assist in algorithm performance evaluation. The final algorithm selection was based both on computational complexity and algorithm performance.
Effective adaptive spatial-temporal technique for clutter rejection in IRST, A. G. Tartakovsky, Univ. of Southern California [4048-09]
In IRST applications, cluttered backgrounds are typically much more intensive than the equivalent sensor noise and intensity of the targets to be detected. This necessitates the development of efficient clutter rejection technology for track initialization and reliable target detection. Experimental study shows that the best existing spatial filtering techniques allow for clutter suppression up to 10 dB, while the desired level (for the reliable detection/tracking) is 25-30 dB or higher. This level of clutter suppression can be achieved only by implementing spatial-temporal rather than spatial filtering. In addition, the clutter rejection algorithm should be supplemented by a jitter compensation technique. Otherwise, due to the blurring effect, temporal filtering cannot be applied effectively. This paper discusses a novel adaptive spatial-temporal technique for clutter rejection. The algorithm is developed on the basis of the application of robust and adaptive methods that are invariant to the prior uncertainty with respect to statistical properties of clutter and adaptive with respect to its variability. Results of applying this algorithm to real shipboard and airborne IRST data show that the algorithm gives a substantial gain compared to the best existing spatial techniques.
Multispectral detection of dim slightly extended targets in heavy clutter, P. F. Singer, D. M. Sasaki, Raytheon Co. [4048-10]
The spectral signature of a target is typically unknown apriori because of its dependence upon environmental conditions (e.g., sun angle, atmospheric attenuation and scattering), factors effecting the reflectivity and emissivity of the targets surface (dirt, dust, water, paint, etc.) and recent operating history (hot or cold engine, exhaust parts, wheels or tracks, etc.). Because of the high variability of the spectral signature of a target, multispectral detection typically detects spectral anomalies. For example, the canopy of a helicopter hovering in front of tree clutter may glint in the midwave infrared band while the reststrahlen spectral feature of the fuselage paint occurs in the longwave infrared band. Both of these are spectral anomalies relative to the tree clutter. If the target is slightly extended so that it subtends more than one pixel, the spectral anomalies by which the target may be detected will not be spatially collocated. This effectively lowers the ROC (receiver operating characteristic) curve of the detection process. This paper derives the ROC curves for several alternative solutions to this problem. One solution considers all possible spectral n-tuples within a small region. One of these n-tuples would likely contain all of the spectral anomalies of the target. Another solution is to apply a spatial maximum operator to each spectral band prior to the anomaly detector. This also combines all the spectral anomalies from the target into a single n-tuple. These and other methods have the potential to increase PD but an increase in PFA will also occur. The ROC curves of these solutions to the problem of detecting slightly extended targets are derived and compared to establish relative levels of performance.
Post-processing and moving platform performance of point target detection sinusoidal filters, C. E. Caefer, J. Silverman, S. DiSalvo, R. W. Taylor, Air Force Research Lab. [4048-11]
In previous conferences (1995, 1998), we described a powerful class of temporal filters, which have excellent signal to clutter gains in evolving clouds. The generic temporal filter is a zero-mean damped sinusoid, implemented recursively. The full algorithm, a triple temporal filter (TTF), consists of a sequence of two zero-mean damped sinusoids followed by an exponential averaging filter with spatial damping at strong edges. We focus here on the post-processing and thresholding of the outputs of the filter algorithms. Post-processing on each output frame is done by a simple spatial algorithm which searches for maximum linear or pseudo-linear streaks made up of three linked pixels. The threshold is based on a simple level-occupancy (binary) histogram in which the first gap of 4 empty levels is determined and a threshold established based on this gap and the number of occupied levels in the histogram above the gap. The post-processing and thresholding of the filter outputs have also been implemented in hardware. Preliminary flight tests on a small aircraft of the algorithms in real-time operation demonstrate the viability of the approach on a moving platform. Specific examples and a video of the real-time performance on a fixed and moving platform will be presented at the conference.
Evaluation of two-color missile detection algorithms against real backgrounds, F. O. Baxley, Veridian Inc.; R. B. Sanderson, Air Force Research Lab.; J. B. Montgomery, Mercer Engineering Research Ctr. [4048-12]
NO ABSTRACT PROVIDED
Interactive banks of Bayesian matched filters, B. L. Rozovskii, A. Petrov, Univ. of Southern California [4048-13]
The 3D matched filter proposed by I.Reed et al. provides a powerful processing technique for detecting moving low observable targets. This technique is a centerpiece of various track-before-detect systems. However, the 3D matched filter is designed for constant velocity targets and its applicability to more complicated patterns of target dynamics is not obvious. In this paper the 3D Bayesian matched filter is extended to the case of switching multiple models of target dynamics. To handle these models we developed interactive banks of Bayesian matched filters and compare these technology with the IMM approach. A computationally efficient (real time) algorithm for detection and tracking of low observable agile targets is presented. Explicit quantitative performance characteristics (error estimates, confidence areas, etc.) are discussed. The algorithm is implemented as an TBD subsystem for IRST, however the general methodology is equally applicable for other imaging sensors ( SAR, HRRR, etc. ).
Fast small target detection with a multiresolution attention mechanism, Y. Wang, G. Wang, X. Wang, T. Zhang, Huazhong Univ. of Science and Technology (China) [4048-14]
In this paper, an approach for fast small target detection based on multi-resolution attention mechanism is provided. This paper discusses topics of: multiresolution sampling model; index information generating (interesting point searching); next fixation point determination; target detection and robustness to noise from imaging and image edition. Implementing attention mechanism in motion small target detection is also discussed.
In the paper, variance of the neighboring nodes in the periphery is used to form a saliency map of the image, point with higher saliency is of greater possibility to be on or near a boundary, while points on boundary are the "interesting points" or "index points" in small target detecting task. A two-step saccade is adopted to determine whether there is a target near the interesting point, if do, locate it. In the paper, the sampling model is introduced, and then the "index point" detecting and the following saccade and analysis process are discussed. At the end of the paper, experimental results of small target detection in sea surface images with some kind of noise are presented. Those demonstrate our approach can effectively and correctly find small target and is robust to imaging and image edition noise. Comparison of performance with our approach and that of Takacs's Dynamic and Multiresolution Attention is also presented.
Small moving target detection using adaptive prediction and motion compensation, A. Cetin, Bilkent Univ. (Turkey); H. Senel, Anatolian Univ. (Turkey); O. N. Gerek, Bilkent Univ. (Turkey) [4048-68].
This is a schedule change -- was previously scheduled as a Standby/Oral Presentation.
Conference Overview, O. E. Drummond, Consulting Engineer [4048-00]
Track testing for single targets in clutter, P. K. Willett, Y. Bar-Shalom, Univ. of Connecticut [4048-16]
NO ABSTRACT PROVIDED
Track grouping to enhance moving target exploitation, S. Sassman, Northrop Grumman Corp. [4048-17]
The Northrop Grumman Track Grouper provides an effective solution to the track grouping problem because it successfully forms track aggregates that decrease display clutter and enhance the battle management picture. The Track Grouper is a formation group tracking algorithm that makes track-to-track comparisons to create and update group membership. The benefits of this approach are conservation of computer resources and preservation of individual target positions. The Track Grouper reduces the adverse effects of measurement mis-correlations and false alarms because it uses estimated tracks instead of noisy sensor measurements. The Track Grouper uses a series of kinematic gates and three primary sub-functions (assign, split, and merge) to determine group membership and maintain group ID, history, and ancestry. The Track Grouper successfully overcomes the challenges of group tracking and provides a key battle management tool that enhances Moving Target Exploitation.
Multiple-hypothesis multiple-model line tracking, D. W. Pace, M. W. Owen, ORINCON Corp. [4048-18]
Passive sonar signal processing generally includes tracking of narrowband and/or broadband signature components observed on a Lofargram or on a Bearing-Time-Record (BTR) display. Fielded approaches to date have been recursive and single-hypothesis-oriented Kalman- or alpha-beta filters, with no mechanism for considering tracking alternatives beyond the most recent scan of measurements. While adaptivity is often built into the filter to handle changing track dynamics, these approaches are still extensions of single target tracking solutions to a multiple target tracking environment.
This paper describes an application of multiple-hypothesis, multiple target tracking technology to the sonar line tracking problem. A Multiple Hypothesis Line Tracker (MHLT) is developed which retains the recursive minimum-mean-square-error tracking behavior of a Kalman Filter in a maximum-a-posteriori delayed-decision multiple hypothesis context. Multiple line track filter states are developed and maintained using the interacting multiple model (IMM) state representation. Further, the data association and assignment problem is enhanced by considering line attribute information (line bandwidth and SNR) in addition to beam/bearing and frequency fit. MHLT results on real sonar data are presented to demonstrate the benefits of the multiple hypothesis approach. The utility of the system in cluttered environments and particularly in crossing line situations is shown.
Maneuver tracking algorithms for AEW electronically scanned radar target tracking applications, R. Schutz, B. Engelberg, W. Soper, T. Sondi, R. Mottl, Northrop Grumman Corp. [4048-19]
NO ABSTRACT PROVIDED
Adaptive threshold control in an autonomous sensor field, D. M. Klamer, M. W. Owen, ORINCON Corp. [4048-20]
NO ABSTRACT PROVIDED
Track partitioning technique for electronically scanned radars, G. E. Brown, W. D. Blair, Georgia Tech Research Institute [4048-54]
This is a schedule change -- was previously scheduled as a Standby/Oral Presentation.
*SAR real-time processor, J. Huang, J. Wu, S. Huang, Univ. of Electronic Science and Technology of China [4048-07]
* Ultrasonic velocity measurements of Cu(I) and some tetraalkyl ammonium salts in binary organic mixtures to study various structural effects, P. Singh, Government College for Girls (India) [4048-52]
* Integrated mixture reduction data association filter, B. Ristic, Defence Science and Technology Organisation (Australia) [4048-53]
* Solving the multiple dimension assignment problem using dual variables as Lagrange multipliers, K. J. Wallenstein, Northrop Grumman Corp. [4048-55]
* Multispectral track features for the general case of an unknown target spectral signature, P. F. Singer, Raytheon Co. [4048-58]
* Using TOTS for multisensor end-to-end ballistic missile tracking, P. F. Easthope, Advanced System Architectures Ltd. (UK) [4048-59]
* Kalman filter versus IMM estimator: when do we need the latter? T. Kirubarajan, Y. Bar-Shalom, Univ. of Connecticut [4048-60]
* Integration of features and attributes into multiple target tracking, O. E. Drummond, Consulting Engineer [4048-70]
Poster Presentations
* Negative correlation and optimal tracking with Doppler measurements, Y. Bar-Shalom, Univ. of Connecticut [4048-61]
* Reflected natural light polarization and spectroenergetic characteristics estimation in ultraviolet region, Y. P. Shumilov, P. A. Bakut, O. Ershova, Institute of Problem of Informational Resources (Russia) [4048-63]
* Detection and tracking of dim point targets in staring infrared image sequences from an airborne platform, C. Wu, Y. Zhang, J. Wang, Shanghai Institute of Technical Physics (China) [4048-64]
* Multiple target tracking using QDS gating, L. Hu, Y. Chen, S. Zou, Zhejiang Univ. (China) [4048-65]
* Sensor management for tracking maneuvering targets in the presence of false alarms, D. E. Penny, M. Williams, Defence Evaluation and Research Agency Malvern (UK) [4048-66]
Survey of maneuvering target tracking, X. R. Li, V. P. Jilkov, Univ. of New Orleans [4048-22]
NO ABSTRACT PROVIDED
Tracking evasive move-stop-move targets with an MTI radar using a VS-IMM estimator, T. Kirubarajan, Y. Bar-Shalom, Univ. of Connecticut [4048-23]
In this paper we present the design of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of evasive ground targets using Moving Target Indicator (MTI) reports obtained from an airborne sensor. In order to avoid detection by the MTI sensor, the targets use a "move-stop-move" strategy, where a target deliberately stops or moves at a very low speed for some time before accelerating again. Under these conditions, the use of an estimator, which does not take care of "move-stop-move" motion explicitly, can result in broken tracks --- such a tracker terminates an existing track when it does not get any measurements from the corresponding target and initiates a new one when measurements from that target are received.
The tracker proposed in this paper handles evasive "move-stop-move" motion via the Variable Structure Interacting Multiple Model (VS-IMM) estimator, where the mode set (kinematic models used in the filter) of the tracker is adaptively varied for each target at each revisit time. A simulated scenario is used to illustrate the selection of design parameters and the operation of the tracker. Performance measures are presented to contrast the benefits of the VS-IMM estimator, which uses the "stopped-target" model, over a Kalman filter and a standard IMM estimator.
Variable structure interacting multiple model filter (VS-IMM) for tracking targets with transportation network constraints, B. J. Noe, N. Collins, Northrop Grumman Corp. [4048-24]
A Ground Moving Target Indicator (GMTI)is developed using a Variable Structure Interacting Multiple Model Filter (VS-IMM). Current trackers use road network database information to either condition GMTI measurements, thereby altering the detection report, or constrain tracks formed on measurements near roads to the road network, thus making a hard decision about the location of the target. The VS-IMM allows for soft decisions about which road a target is possibly located on. The VS-IMM filter developed adaptively adds and deletes road models based upon history of measurement data or extrapolated tracks. As measurements are associated with existing tracks, a search of possible road segment models is performed to either add or delete road segment models as required. As targets on roads approach junctions, additional potential road segment models are added, as the targets pass the intersection only the most likely model is retained. When a target begins to move into a road segment that is obscured, a model is added that modifies the filter estimate and likelihood according to a hidden target model. The state estimates from the possible road models are combined to produce the composite state estimate for the VS-IMM Filter.
Extended Kalman filter for small resolved targets in a turbulent atmosphere, S. H. Roszkowski, Raytheon Systems Co. [4048-25]
NO ABSTRACT PROVIDED
Nonlinear filtering for ground target applications, K. D. Kastella, ERIM International Inc. [4048-26]
NO ABSTRACT PROVIDED
Novel branching particle method for tracking, D. Ballantyne, H. Chan, M. Kouritzin, MITACS-Pints and Univ. of Alberta (Canada) [4048-27]
Particle approximations are used to track a maneuvering signal given only a noisy, corrupted sequence of observations, as are encountered in target surveillance. The signal exhibits nonlinearities that preclude the optimal use of a Kalman filter. It obeys a stochastic differential equation in a seven-dimensional state space, one dimension of which is a discrete maneuver type. The maneuver type switches as a Markov chain and each maneuver identifies a unique SDE for the propagation of the remaining six state parameters. Observations are constructed by projecting the target state into a polygon representation in a two-dimensional space and incorporating noise.
A new branching particle filter is introduced and compared with two existing particle filters. The filters simulate a number of independent particles, each of which move with the stochastic law of the target. Each filter provides an approximated estimate of the target state given all back observations.
All three particle filters are provably optimal as the number of particles approaches infinity, but differ in how well they perform with finite particles. Using the known ground truth, the RMS errors of the estimated densities from the three filters are compared. Relative tracking power of the filters is quantified for this target at varying
sizes.
Comparison of the particle filter with range-parameterized and modified polar coordinate EKFs for angle-only tracking, S. Arulampalam, B. Ristic, Defence Science and Technology Organization (Australia) [4048-28]
The tracking performance of the Particle Filter is compared with that of the Range-Parameterised EKF (RPEKF) and Modified Polar coordinate EKF (MPEKF) for a single-sensor angle-only tracking problem with ownship maneuver. The Particle Filter is based on representing the required density of the state vector as a set of random samples with associated weights. This filter is implemented for recursive estimation, and works by propagating the set of samples, and then updating the associated weights according to the new received measurement. The RPEKF, which is essentially a weighted sum of multiple EKF outputs, and the MPEKF are known for their robust angle-only tracking performance. This comparative study shows that the Particle Filter performance is the best, although the RPEKF is only marginally worse. The superior performance of the Particle Filter is particularly evident for high noise conditions where the EKF type trackers generally diverge. Also, the Particle Filter and the RPEKF are found to be robust to the level of a priori knowledge of initial target range. On the contrary, the MPEKF exhibits degraded performance for poor initialisation.
IMM algorithm based on a hybrid bootstrap filter, Y. Boers, H. Driessen, Hollandse Signaalapparaten BV (Netherlands) [4048-29]
In this paper we present a new method for multiple model filtering. This method is a combination of the IMM filter and the particle filter. The IMM part is able to deal with the model switching behaviour, whereas the particle filter can deal with nonlinearities in the dynamics and measurements and possible non Gaussian noise within a certain mode. In an illustrative example we will show that this method is superior to the standard IMM
filter.
Search for tractable Bayesian multitarget filters, R. P. Mahler, Lockheed Martin Tactical Defense Systems [4048-30]
NO ABSTRACT PROVIDED
Accuracy of projections in precision tracking of surface targets, P. J. Shea, T. Zadra, D. Klamer, E. Frangione, R. Brouillard, ORINCON Corp. [4048-31]
The ability to provide an accurate view of a region of interest is standard among existing tracking systems. The extension of this capability to include accurate projections of the targets to future times increases the complexity of the problem. However, this ability to predict future locations is an important problem due to the inherent time latency that is present from sensor to shooter. Because of this, a major requirement of the tracking system is that it must be able to use available information to accurately predict future target locations. The focus of this paper is to describe an improved state estimation technique that incorporates road information by using a Variable Structure IMM. Furthermore, this approach will identify and account for stopped or stationary moving targets. Finally, some preliminary performance results will be presented.
Two-dimensional assignment algorithms for tracking combat maneuvering aircraft with an electronically scanned radar, W. D. Blair, G. E. Brown, Georgia Tech Research Institute [4048-32]
NO ABSTRACT PROVIDED
Moving target tracking using multiple sensors, R. Bottone, M. D. Lodaya, Northrop Grumman Corp. [4048-33]
Multiple Sensors improve tracking performance. The Kinematic Automatic Tracker (KAT) may drop a track when the Joint STARS aircraft is turning or the track is screened. In situation like this multiple sensors can improve tracking performance. Two algorithms for tracking moving targets using multiple sensors are developed. Both algorithms are based on modifying the existing KAT architecture. The first approach is based on use of the best sensor's Moving Target Indicators (MTIs) when the sensors have overlapping dwells. The second and more general approach uses the report-to-track fusion algorithm. The first approach is frame-based (with multiple dwells), whereas the second approach is dwell-based. The tracking results are presented for the first algorithm for a two-sensors Korean scenario with increasing complexity. We also show the advantages of using multiple sensors for tracking using a simulated Korea scenario. For the report-to-track fusion algorithm, we present preliminary tracking results for a simulated scenario using two UAV sensors.
Adaptive early-detection ML/PDA estimator for LO targets with EO sensors, M. R. Chummun, Motorola; T. Kirubarajan, Y. Bar-Shalom, Univ. of Connecticut [4048-34]
The batch Maximum Likelihood Estimator, combined with the Probabilistic Data Association Algorithm (ML-PDA), has been shown to be effective in acquiring low observable non-maneuvering targets in the presence of heavy clutter. The use of signal strength or amplitude information (AI) in the ML-PDA estimator facilitates the acquisition of weak targets. In this paper we present an adaptive algorithm, which uses the ML-PDA estimator with AI in a sliding-window fashion, to detect possibly maneuvering high-speed targets in heavy clutter using electro-optical (EO) sensors. The initial time and the length of the sliding-window are adjusted adaptively according to the information content of the received measurements. A track validation scheme via hypothesis testing is developed to confirm the estimated track, that is, the presence of a target, in each window. The sliding-window ML-PDA approach, together with track validation, enables early detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance and the handling of maneuvering targets. The time-varying SNR of target returns, which is modeled as Swerling~I fluctuating, is estimated online and the detection threshold is selected adaptively based on a Constant Probability of Detection (CPD). The Cramer-Rao Lower Bound, which quantifies the accuracies attainable by an unbiased estimator for this low-observable estimation problem (in the presence of false alarms), is also derived.
A Standby Paper Will Be presented at This Time -- Which One Is TBD.
Initiating 3D air target tracks from 2D naval radar sensor reports, P. Valin, Lockheed Martin Canada; H. Aurag, M. Kjiri, Univ. de Montréal (Canada) [4048-36]
The topic studied here involves target tracking of aircraft using naval radar, and can be also used in civilian applications such as airport traffic management. The aim is to initiate a 3-D track from lower-dimensional 2-D radar contact data for 3-D track initiation and/or promotion. Because it is meant to be used at long ranges, the main assumption is that the target is performing rectilinear motion at a given altitude. The solution to this problem will facilitate track management, as all tracks will eventually be three-dimensional. The two cascaded algorithms presented here consist in first determining speed and altitude independently of the actual trajectory, then determining the actual trajectory given the best-fit value for the altitude. The algorithms are shown to work perfectly for noiseless data and adequately enough for typical naval radar parameters. The added sensory components will help resolve the association problem in multiple target scenarios by providing altitude information hidden from the sensors and revealed only through this mathematical modeling and its related algorithmic processing. In addition, the fact that one can deduce speed and altitude at the early stages of tracking permits the elimination of many platform identifications at the outset of the Multi-Sensor Data Fusion process.
Performance comparison of 2D assignment algorithms for assigning truth to measured tracks, M. Levedahl, Raytheon [4048-67]
The processing time requirements and accuracy of several algorithms for solving the 2-d linear assignment problem are compared, along with their accuracy given either random or biased measurement errors. The specific problem considered is that of assigning measurements to truth objects using costs that are the chi-squared distances between the measurements and objects. Performance comparisons are provided for the algorithms implemented both in C as well as in the interpretive Matlab language.
An optimal assignment algorithm is preferred if biased measurement errors are present, and the Jonker-Volgenant-Castanon (JVC) algorithm is the preferred approach considering both average and maximum solution time. The Auction algorithm finds favor due to being both efficient as well as easy to understand, but is never faster and often much slower than the JVC algorithm. Both algorithms are dramatically faster than the Munkres algorithm. The greedy nearest neighbor algorithm is an ad hoc solution developed to provide a sub-optimal but unique solution more cheaply than the optimal assignment algorithms. However, the JVC algorithm is as fast as the greedy for simple problems, marginally slower at hard problems, and is vastly more accurate in the presence of measurement biases. Finally, results show very little advantage to using integer rather than floating point cost matrices.
Prototype system for multisensor tracking with sensor bias correction, P. F. Easthope, Advanced System Architectures Ltd. (UK) [4048-38]
NO ABSTRACT PROVIDED
Bias estimation for multisensor tracking, E. Sviestins, CelsiusTech Systems AB (Sweden) [4048-39]
Efficient bias removal from sensor data is very important for successful multisensor tracking. Unfortunately this is quite difficult to accomplish when many 'bias parameters' (range offset, sensor locations, tilt angles, etc.) are involved because of their limited observability. The paper presents a method that largely avoids these problems by the inclusion of a priori uncertainties.
The algorithm has several attractive properties: (1) It estimates a large number of bias parameters for each sensor. (2) It handles a large number of sensors. (3) It is based on comparison of targets of opportunity so there is no need for fixed calibration points like test transponders. (4) It works with passive as well as active sensors. (5) There is no need to denote one of the sensors as 'master' or 'reference'. (6) The sensor measurements do not have to be made simultaneously. (7) The algorithms run in real-time. (8) The first estimates are produced quickly, and are then subsequently improved. (9) The algorithms handle time dependence of the bias parameters by including process noise, individual for each bias parameter. (10) The uncertainties of the bias parameter estimates are obtained. (11) The algorithms are complete and running in operational tracking systems. The paper describes the basic algorithms, and discusses the observability issues with the help of a number of simulated scenarios.
Comparison of methodologies for mitigating coordinate transformation bias in target tracking, M. D. Miller, RAND Corp.; O. E. Drummond, Consulting Engineer [4048-40]
Nonlinearities in coordinate transformation equations introduce bias effects that, unless corrected, can affect the statistical fidelity of parameter estimates. Several correction methods have been studied, differing both in their algebraic form (additive versus multiplicative) and in their underlying statistical basis (fixed truth versus fixed measurement assumptions). This paper compares alternative approaches for mitigating the bias induced in the transformation from sensor range-azimuth-elevation angle coordinates to Cartesian x-y-z coordinates. Comparisons are made initially for a static tracking environment involving coordinate transformations at a single measurement time. The comparisons are then extended to a time-sustained tracking period in which measurements are recorded and passed through a Kalman filter to produce a track estimate. Finally, the paper discusses the application of the methodologies to more general variable transformations for which the bias corrections do not necessarily have closed-form mathematical realizations.
IMM estimator-based tracking and resource allocation for highly maneuvering closely spaced targets with multiple sensors, A. Sinha, T. Kirubarajan, Y. Bar-Shalom, Univ. of Connecticut [4048-57]
This is a schedule change -- was previously scheduled as a Standby/Oral Presentation.
Comparison of centralized and decentralized tracking algorithms using simulated air-traffic data, H. Chen, T. Kirubarajan, Y. Bar-Shalom, Univ. of Connecticut [4048-42]
In this paper, we compare the performances of centralized and decentralized (or distributed) tracking architectures using a broad set of Air Force mission scenarios. The tracking accuracy at platform and global levels is evaluated for different target maneuver levels, target/platform separations. Kalman filter and IMM estimators with different target kinematic models are compared in terms of root mean square (RMS) position error, RMS velocity errors and track purity. Computational load of different estimators is also used for performance comparison. The centralized solution for each scenario is derived as a performance benchmark for this Air Force Distributed Tracking Architecture (DTA) study. Data association options via 2-dimensional assignment and the Nearest Neighbor Joint Probabilistic Data Association (NNJPDA) technique are also considered. In addition to the effects of target maneuvers and target separations, the effects of varying false alarm density and varying sensor revisit intervals are also evaluated. To evaluate the performances of the various estimator options, 24 scenarios, with varying maneuvering levels, measurements error statistics, revisit intervals and target/platform separations are used. Scenarios considered include target maneuvers up to 3.5g and use measurements from up to 4 sensors. Based on simulation results, appropriate estimator/data association options are recommended for different scenario configurations.
Decentralized track fusion in dynamic networks, D. Nicholson, R. H. Deaves, British Aerospace (UK) [4048-43]
Decentralized estimation in dynamic battlespace networks is considered. The decentralized `plug and play' architecture represents a highly flexible and robost C2 paradigm. However, it is threatened by the problem of redundant data in multi-path networks, which can cause inconsistent estimates. This paper presents two approaches to the problem: a Kalman based fusion of local network information and a Covariance Intersection based fusion of global information. Neither performs uniformly best across a wide range of scenario types. This motivates a hybrid solution. This paper reports simulations of some decentralized track fusion scenarios. These illustrate the problem and provide an initial performance assessment of its proposed solutions.
Track-to-track association in decentralized tracking systems with feedback, A. Malmberg, M. Karlsson, T. Jensen, Saab AB (Sweden) [4048-44]
In air combat, information advantage is vital for the success of the operation and modern fighter aircraft have extensive sensor suites to track other objects. Since system modularity is a key issue, a decentralized tracking approach is preferable. Furthermore, in order to improve the sensor tracking performance, it is often desired to feed back information to the sensors.
In this paper, track-to-track association in such a decentralized tracking system with feedback is addressed. In a system without feedback, the track-to-track association algorithm bases its conclusions on the assumption that the estimation errors of the tracks from different local trackers uncorrelated. However, when fed back information is used in the local trackers, this assumption is not valid, since the sensor tracks then consist of common information.
System configurations that deal with this problem are proposed and tested in a the application. One approach is to extract the uncorrelated information from the sensor data and use that in the association process. Another approach is to keep parallel trackers in the sensors that only contain the local information. Also, a track-to-track association algorithm that recursively uses information from several time steps is proposed. The result is an algorithm that improves the performance and gives a more stable solution.
Multiple-target tracking in dense noisy environments: a probabilistic mapping perspective, K. M. Tao, R. Abileah, J. D. Lowrance, SRI International [4048-45]
A new approach is taken to address the various aspects of the multiple-target tracking (MTT) problem in dense and noisy environments. Instead of fixing the trackers on potential targets as the conventional trackers do, this new approach is fundamentally different in that an array of parallel-distributed "trackers" is laid in the search space. The difficult data-track association problem that has challenged the conventional trackers, especially in noisy environments, now becomes a non-issue with this new approach. By partitioning the search space into "cells," this new approach, called PMAP (probabilistic mapping), dynamically calculates the spatial probability distribution of targets in the search space via Bayesian updates. General Markov-chain type target motion model is used to account for target motion between frames. This framework can effectively handle data from multiple sensors and incorporate contextual information, such as terrain and weather, by performing a form of Evidential Reasoning. Used as a pre-filtering device, the PMAP is shown to eliminate noise-lie false alarms effectively. leaving the downstream "tracker" algorithm a much easier task to perform. With PMAP it is now possible to lower the detection threshold and to enjoy high Probability of Detection and low Probability of False Alarms at the same time. The feasibility of using PMAP to track a specific target in an end-game scenario is also discussed. Both real and simulated data are used to illustrate the PMAP performance.
Implementation and analysis of the decomposition-fusion ECCM technique, B. J. Slocumb, P. D. West, Georgia Tech Research Institute; X. R. Li, Univ. of New Orleans [4048-46]
NO ABSTRACT PROVIDED
New merging formula for multiple model trackers, D. D. Sworder, Univ. of California/San Diego; J. E. Boyd, Cubic Defense Systems, Inc. [4048-47]
The quality of multiple model estimators can be improved with multisensor fusion. This paper contrasts the performance of three multiple model algorithms. It is shown that the simplest is adequate in high signal-to-noise environments. The more sophisticated warrant attention when the observations are ambiguous.
Time-recursive number-of-tracks estimation for MHT, K. M. Buckley, J. Bradley, R. Perry, Villanova Univ. [4048-48]
In this paper we address the issue of measurement-to-track association within the framework of multiple hypothesis tracking (MHT). Specifically, we generate a maximum a posteriori (MAP) cost as a function of the number of tracks K. This cost is generated, for each K, as a marginalization over the set of hypothesized track-sets. The proposed algorithm is developed based on a trellis diagram representation of MHT, and a generalized list-Viterbi algorithm for pruning and merging hypotheses. Compared to methods of pruning hypotheses for either MHT or Bayesian multitarget tracking, the resulting Viterbi MHT algorithm is less likely to incorrectly drop tracks in high clutter and high missed-detection scenarios. The proposed number-of-tracks estimation algorithm provides a time-recursive estimate of the number of tracks. It also provides track estimates, allows for the deletion and addition of tracks, and accounts for false alarms and missed detections.
Performance metrics for multiple-sensor multiple-target tracking, R. L. Rothrock, SPARTA, Inc.; O. E. Drummond, Consulting Engineer [4048-49]
Because there may be misassociations, soft-decision association, and/or spurious tracks due to false detections, performance evaluation of multiple target tracking is more complex than evaluating filter performance. This paper presents a methodology and performance metrics to evaluate tracking in the presence of these and other complications. The goal is to evaluate the various aspects of tracking performance that are of concern to the users.
The emphasis of this paper is on evaluating the performance not only for single sensor tracking, but also for distributed trackers combining data from multiple, distributed sensors. Included are the equations for computing metrics to evaluate completeness, timeliness, track continuity, ambiguity, accuracy, and cross-platform commonality. Cross-platform commonality includes metrics to evaluate how consistent the target tracks are across global trackers at different locations.
Group tracking with limited sensor resolution and finite field of view, D. J. Salmond, Defence Evaluation and Research Agency Farnborough (UK); N. J. Gordon, Defence Evaluation and Research Agency Malvern (UK) [4048-62]
In this paper we address two important features of measurement sensors that are often ignored in the development of tracking filters: limited resolution and finite field of view (FOV). These effects are difficult to accommodate in traditional Kalman-based tracking filters because they introduce gross non-linearities in the form of ``hard edges" on probability distributions. We examine a group tracking problem coupled with the above practical sensor limitations. In particular, we employ the group tracking model reported at last years conference. This model exploits the fact that targets tend to move in a formation pattern rather than as completely independent entities. This group motion is modelled as a common ``bulk" effect superposed on an independent motion for each individual target. The bulk motion may allow for translation, rotation and scaling of the target group. Stressing scenarios with a high degree of clutter and missing measurements (leading to measurement association uncertainty) are of particular interest. A particle filter has been applied to this problem. In this approach the probability distribution of the problem state vector is represented by a set of random samples or ``particles". This method has the advantages for the above tracking problem that awkward hypothesis management is avoided, complex and nonlinear representations of sensor resolution may be applied directly to the target samples without approximation and finite FOV effects are easily implemented. We present a general description of the tracking problem including the two component dynamics model and with particular emphasis on the measurement process. We summarise the formal Bayesian solution and an implementation via a particle filter, including hypothesis construction. A simulation example to illustrate the operation of the filter is given.
Update with out-of-sequence measurements in tracking: exact solution, Y. Bar-Shalom, Univ. of Connecticut [4048-51]
In target tracking systems measurements are typically collected in ``scans'' or ``frames'' and then they are transmitted to a processing center. In multisensor tracking systems that operate in a centralized manner there are usually different time delays in transmitting the scans or frames from the various sensors to the center. This can lead to situations where measurements from the same target arrive out of sequence. Such ``outofsequence'' measurement (OOSM) arrivals can occur even in the absence of scan/frame communication time delays. The resulting ``negativetime measurement update'' problem, which is quite common in real multisensor systems, was solved approximately by neglecting the process noise in the ``backward prediction'' or retrodiction. In the standard case, the (forward) state prediction can be easily carried out, since the process noise, because of its whiteness, is independent of the current state. However, in retrodiction this independence does not hold anymore. The standard smoothing algorithms cannot be used because the ``time stamp'' of the measurement is, in general, arbitrary. The results of Blair and Blackman accounted only partially for the process noise. In view of this, the exact state update equation for such a problem is presented. The three algorithms are compared on a number of realistic examples, including a GMTI (ground moving target indicator) radar case.