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A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking

A multiple model SNR/RCS likelihood ratio score for radar-ba...

引用

17th conference on signal and data processing of small targets

作者： Slocumb, BJ Klusman, ME Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. the classical approach uses a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an "auxiliary score" that augments the kinematic score. this paper develops a non-kinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. the formulation requires an estimate of the target mean RCS, and a key challenge is the trackina of the mean RCS through significant "jumps" due to aspect dependencies. A novel multiple model approach is used track through the RCS jumps. three solution are developed: one based on an a-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.

关键词： feature-aided tracking RCS tracking multiple model signal score swerling fluctuation

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A flexible implementation for Doppler radar to verify various base-band array signal processing algorithms - art. no. 591307

A flexible implementation for Doppler radar to verify variou...

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17th conference on signal and data processing of small targets

作者： Yang, E Lee, J Jung, B Chun, J Korea Adv Inst Sci & Technol Comp Sci Lab Taejon 305701 South Korea

ISBN: (纸本)0819459186

We describe a flexible hardware system of the Doppler radar which. is designed to verify various baseband array signal processing algorithms. In this work we design the Doppler radar system simulator for baseband signal processing in laboratory level. Based on this baseband signal processor, a PN-code pulse doppler radar simulator is developed. More specifically, this simulator consists of an echo signal generation part and a signal processing part. For the echo signal generation part, we use active array structure with 4 elements, and adopt baker coded PCM signal in transmission and reception for digital pulse compression. In the signal processing part, we first transform RF radar pulse to the baseband signal because we use the basebands algorithms using IF sampling. Various digital bearnforming algorithms can be adopted as a baseband algorithm in our simulator. We mainly use Multiple Sidelobe Canceller (MSC) with main array antenna elements and auxiliary antenna elements as beamforming and sidelobe canceller algorithm. For Doppler filtering algorithms, we use the FFT. A control set is necessary to control overall system and to manage the timing schedule for the operation.

关键词： Doppler radar FFT beamforming sidelobe canceller CFAR pulse compression

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Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications - art. no. 59130Z

Performance evaluation of time-weighted backvalues least squ...

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17th conference on signal and data processing of small targets

作者： Imam, N Barhen, J Glover, CW Oak Ridge Natl Lab Comp Sci & Math Div Ctr Engn Sci Adv Res Oak Ridge TN 37831 USA

ISBN: (纸本)0819459186

the rapid growth and increasing sophistication of airborne surveillance technology have spurred intense research efforts in the development and implementation of tracking algorithms capable of processing a large number of targets using multi-sensor data. In this paper, a, novel tracking algorithm, the NOGA tracker, is presented and compared with the more conventional Time-Weighted Backvalues Least Squares (TWBLS) estimator for accuracy (numerical and phenomenological), ease of implementation, and time performance. the NOGA tracker combines model predictions and sensor measurements to produce best estimates for quantities of interest. the state estimator model used for NOGA is a simple second order auto-regression which is combined with an uncertainty reduction scheme involving nonlinear Lagrange optimization process in which the inverse of a global covariance matrix is used as the natural metric for the Bayesian inference that underlies the combining process. the NOGA tracker explicitly incorporates sensor and model uncertainties in the estimation process, and uses model sensitivities to propagate the associated covariance matrices accurately and in a systematic way.

关键词： Uncertainty reduction track fusion sensor fusion multi-sensor tracking auto-regressive

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Network centric MFA tracking architecture based on soft-level data association - art. no. 591316

Network centric MFA tracking architecture based on soft-leve...

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17th conference on signal and data processing of small targets

作者： Novoselov, R Gadaleta, S Poore, A Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

this work presents a new network centric architecture for multiple frame assignment (MFA) tracking. the architecture improves on earlier network tracking schemes by allowing trackers to broadcast decisions about their local soft-level associations, via the Soft Associated Measurement Reports (SAMRs). the SAMR may be followed by an "Oops" message, if the soft association was incorrect and must be revoked. We show, however, that such revocations are very rare in most scenarios. this paper discusses the implementation of the new algorithm and presents simulation results. Considerable improvements in the consistency of the air picture are demonstrated, owing to the the reduced latency in transmission of measurement-to-track associations. the earlier network architectures, namely, the Centralized MFA, the Replicated Centralized MFA, and the Network MFA on Local and All data, are also discussed in this work, as they form the foundation for the "Oops" algorithm.

关键词： Multiple hypothesis tracking multiple frame assignment distributed tracking architecture

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Comparison of LMITS and MHT algorithms

Comparison of LMITS and MHT algorithms

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17th conference on signal and data processing of small targets

作者： Musicki, D Mallick, M La Scala, B Strange, S Evans, R Univ Melbourne Dept Elect & Elect Engn Melbourne Syst Lab Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

the Multiple Hypotheses Tracking (MHT) algorithm has been shown to have the best tracking performance among existing multi-target tracking algorithms using real world sensors with probability of detection less than unity and in the presence of false alarms. the improved performance of the Multiple Hypotheses Tracking comes at the cost of significantly higher computational complexity. Most Multiple Hypotheses Tracking implementations only form the best global hypothesis. this paper compares the Linear Multitarget Integrated Track Splitting (LMITS) tracking algorithm with the Multiple Hypotheses Tracking algorithm. LMITS has a simpler structure than Multiple Hypotheses Tracking as it decouples local hypotheses and avoids the measurement to multi-track allocation entirely. the number of LMITS hypotheses equals the sum of the number of local hypotheses added to the number of initiation hypotheses. thus LMITS can retain a deeper hypotheses subtree which can result in better performance. We compare tracking performances of LMITS and MHT algorithms using simulated data for multiple maneuvering targets in heavy and non-uniform clutter.

关键词： multi-target multi sensor tracking multiple hypotheses tracking (MHT) linear multitarget (LM) integrated track splitting (ITS) data association

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Equivalence classes of future paths for sensor allocation and threat analysis

Equivalence classes of future paths for sensor allocation an...

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17th conference on signal and data processing of small targets

作者： Svenson, P Swedish Def Res Agcy Div Command & Control Syst Dept Data & Informat Fus SE-16490 Stockholm Sweden

ISBN: (纸本)0819459186

Sensor allocation and threat analysis are difficult fusion problem that can sometimes be approximately solved using simulations of the future movement of adversary units. In addition to requiring, detailed motion models, such simulation also requires large amounts of computational resources, since a large number of possibilities must be examined. In this paper, we extend our previously introduced framework for doing such simulations more efficiently. the framework is based on defining equivalence classes of future paths of a set of units. In the simplest case, two paths are considered equivalent if they give rise to the same set of observations. For sensor management, each considered sensor plan thus entails an equivalence relation on the set of future paths. this can be used to significantly reduce the number of "alternative futures" that need to be considered for the simulation. For threat analysis, the equivalence relation can instead be based on the perceived threat against own units. We describe how the equivalence classes induced by such relations could be used to improve the visualization of threat analysis systems. User interaction can also be used to refine the equivalence classes;we argue that such interaction will be essential for international operations where is it difficult to define actors and targets.

关键词： sensor management resource allocation threat analysis

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Complexity reduction in MHT/MFA tracking

Complexity reduction in MHT/MFA tracking

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17th conference on signal and data processing of small targets

作者： Poore, AB Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

the performance advantage of the multiple frame data association methods over the single frame methods follows from the ability to hold difficult decisions in abeyance until more information is available and the opportunity to change past decisions to improve current decisions. In dense tracking environments the performance improvements of multiple frame methods over single frame methods is very significant, making it the preferred solution for difficult tracking problems. the price that one pays for this performance gain is the computational complexity (NP-hard) of the resulting data association problem. the number of strings of data or arcs that one forms for N frames of data each containing M reports is M(N) wherein we have omitted missed reports. this number grows exponentially with the length of the window (i.e., N). thus, much preprocessing is required to manage memory usage and to achieve good runtime performance. the control of the computations makes use of a variety of techniques including a) bin gating, b) coarse pair and triple point dynamic gating, c) multiframe gating, d) medium gating based on filter prediction gates, e) fine gating based on likelihood ratios, f) track hypothesis pruning, h) problem partitioning, and i) cluster tracking. While this work comments on many of these methods, the goal is to derive methods bin, coarse pair, and multiframe gating in a simple setting as possible.

关键词： Detection and tracking algorithms Sensors Electronic filtering 3D metrology Computing systems Mahalanobis distance Time metrology Calculus data processing signal processing

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Fusion of uncertain combat identity declarations and implication rules using the belief function theory

Fusion of uncertain combat identity declarations and implica...

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17th conference on signal and data processing of small targets

作者： Ristic, B Smets, P Def Sci & Technol Org ISRD Labs 200 Edinburgh SA 5111 Australia

ISBN: (纸本)0819459186

Surveillance systems typically perform target identification by fusing target ID declarations supplied by individual sensors with a prior knowledge-base. Target ID declarations are usually uncertain in the sense that: (1) their associated confidence factor is less than unity, (2) they are non-specific (the true hypothesis belongs to a subset A of the universe 9). Prior knowledge is typically represented by a set of possibly uncertain implication rules. An example of such a rule is: if the target is Boeing 737 than it is neutral or friendly with probability 0.8. the uncertainty auain manifests itself here in two ways: the rule holds only with a certain probability (typically less than 1.0) and the rule is non-specific (neutral or friendly). the paper describes how the fusion of ID declarations and the implication rules can be handled elegantly within the framework of the belief function theory as understood by the transferable belief model (TBM). Two illustrative examples are worked out in details in order to clarify the theory.

关键词： data fusion target identification belief function evidential theory transferrable belief model implication rules

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A study of factors affecting multiple target tracking with a pixelized sensor - art. no. 59130L

A study of factors affecting multiple target tracking with a...

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17th conference on signal and data processing of small targets

作者： Morelande, MR Kreucher, CM Kastella, K Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

Factors affecting the performance of an algorithm for tracking multiple targets observed using a pixelized sensor are studied. A pixelized sensor divides the surveillance region into a grid of cells with targets generating returns on the grid according to some known probabilistic model. In previous work an efficient particle filtering algorithm was developed for multiple target tracking using such a sensor. this algorithm is the focus of the study. the performance of the algorithm is affected by several considerations. the pixelized sensor model can be used with either thresholded or non-thresholded measurements. While it is known that information is lost when measurements are thresholded, quantitative results have not been established. the development of a tractable algorithm requires that closely-spaced targets are processed jointly while targets which are far apart are processed separately. Selection of the clustering distance involves a trade-off between performance and computational expense. A final issue concerns the computation of the proposal density used in the particle filter. Variations in a certain parameter enable a trade-off between performance and computational expense. the various issues are studied using a mixture of theoretical results and Monte Carlo simulations.

关键词： Particles Detection and tracking algorithms signal to noise ratio Sensors Algorithm development Monte Carlo methods Surveillance Particle filters Target detection Solids

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Bayesian tracking with Dempster-Shafer measurements

Bayesian tracking with Dempster-Shafer measurements

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17th conference on signal and data processing of small targets

作者： Mahler, R Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)0819459186

the theory and practice of single-sensor, single-target tracking is well understood when measurement uncertainties are due entirely to randomness. In this case the Bayes filter and its special cases and approximations, such as the Kalman filter, constitute the foundations of tracking. However, the measurement uncertainties in many observation-types (features, natural-language reports, etc.) can axise from ignorance as well as randomness. Approaches such as the Dempster-Shafer (DS) theory claim to address such information but continue to be controversial, especially in tracking. In other papers this year we have shown that this can be attributed to a lack of formal physical modeling techniques. If familiar measurement models axe extended in a natural way, measurement fusion using DS combination can be subsumed within the Bayesian theory. In this paper we apply these results to introduce the "evidential filter," a special case of the Bayes filter applicable whenever measurement uncertainty can be modeled in DS form. We derive closed-form formulas for the evidential filter;and show that data-update using Dempster's combination is a special case of Bayes' rule. We also briefly show how to incorporate the evidential filter into multitarget tracking techniques.

关键词： Filtering (signal processing) Fuzzy logic Electronic filtering Digital filtering Linear filtering Sensors Motion measurement Electroactive polymers Mathematical modeling Motion models

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