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Reducing MHT computational requirements through use of Cheap JPDA methods

Reducing MHT computational requirements through use of Cheap...

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13th conference on signal and data processing of small targets

作者： Quevedo, H Blackman, S Nichols, T Dempster, R Wenski, R Raytheon Syst Co El Segundo CA 90245 USA

ISBN: (纸本)0819441872

Hypothesis formation is a major computational burden for any multiple hypotheses tracking (MHT) method. In particular, a track-oriented MHT method defines compatible tracks to be tracks not sharing common observations and then re-forms hypotheses from compatible tracks after each new scan of data is received. the Cheap Joint Probabilistic data Association (CJPDA) method provides an efficient means for computing approximate hypothesis probabilities. this paper presents a method for extending CJPDA calculations in order to eliminate low probability track branches in a track-oriented MHT method. the method is tested using IRST data. this approach reduces the number of tracks in a cluster and the resultant computations required for hypothesis formation. It is also suggested that the use of CJPDA methods can reduce assignment matrix sizes and resultant computations for the hypothesis-oriented (Reid's algorithm) MHT implementation.

关键词： multi target tracking (MTT) multiple hypothesis tracker (MHT) Cheap Joint Probabilistic data Association (CJPDA) infrared search and track (IRST)

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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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the target profile identification of step frequency MMW radar based on wavelet neural network

The target profile identification of step frequency MMW rada...

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13th conference on signal and data processing of small targets

作者： Li, YH Gao, DT Shen, QH Li, XG Nanjing Univ Dept Elect Sci & Technol Nanjing 210093 Peoples R China

ISBN: (纸本)0819441872

With the increased availability of coherent wide band radar, there has been a renewed interest in the target recognition of MMW frequency step radar. A large bandwidth gives high resolution in range which means target recognition may be possible. In this paper, by integrating wavelet with neural network, a new adaptive wavelet function neural network is proposed. An artificial neural network with wavelet as weight coefficients is developed for pattern recognition. It is inspired by wavelet transform theory and feed forward neural network. the good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping. the wavelet shapes are adaptively computed to minimize an energy function for a specific application of radar targets. the mathematical frame of the neural network is introduced and error back propagation algorithm is used. the procedure of using wavelet neural network for identification is described in detail. Based on the target specific information offered by the range profiles of step frequency MMW radar targets, the wavelet neural network is applied to recognition of three kinds of practical radar targets. We find that we can reliably distinguish for three targets over a range of aspect angle. Experiment results indicate that the new feature vector in low dimension is valuable for target recognition, the wavelet neural network has faster convergence speed and higher correct recognition rate and the noise resistance character is good.

关键词： wavelet neural network multi-resolution analysis target identification radar signal image processing

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Attributes in tracking and classification with incomplete data

Attributes in tracking and classification with incomplete da...

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16th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)081945351X

An obvious use for feature and attribute data is for classification and in combat identification. the term classification is used broadly here to include discrimination, detection, target typing, identification, and pattern recognition. An additional use is in the data (or track) association process to reduce the misassociations, often called feature aided tracking. Previous papers discussed the integration of features and attributes into target track processing in addition to their use in multiple target classification. the distinction is made between feature and attribute data because they are processed differently. the term features applies to data from continuous sample space and attributes applies to data from discrete sample space. the primary concern of this paper is to address processing of attributes when there is incomplete data. For example in fusion of sensor data from distributed sensors, a sensor processor might make a hard decision on whether a target exhibits a specific target class (or attribute value) or not (without providing additional information such as likelihoods or probabilities). In another example, a sensor might distribute the likelihood of the most likely target class (or attribute property) and no information on the other possibilities. Both classification and attribute aided tracking is addressed for these examples of incomplete data. the purpose of this paper is to show the feasibility of a simple approach to dealing with incomplete data.

关键词： multiple target tracking feature aided tracking features attributes categorical features multiple models Bayesian methods and Kalman filter

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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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Network MFA tracking architectures

Network MFA tracking architectures

引用

13th conference on signal and data processing of small targets

作者： Lu, SH Poore, AB Suchomel, BJ Colorado State Univ Dept Math Ft Collins CO 80523 USA

ISBN: (纸本)0819441872

this work presents some initial steps in the development of a decentralized network centric multiple. frame assignment (MFA) class of architecture algorithms that affordably preserves the quality of a centralized architecture across a network of platforms while managing communication loading and achieving a consistent air picture on entities of interest for each platform. In particular, this work discusses four architectures, namely, Centralized, Network MFA Centralized, Network MEA, on Local data and Network Tracks, and Network MFA on All data and Network Tracks and the results of extensive computations with the architectures.

关键词： centralized architecture network MFA centralized network MFA on local data and network tracks network MFA on all data and network tracks

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On expectation maximization applied to GMTI convoy tracking

On expectation maximization applied to GMTI convoy tracking

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14th conference on signal and data processing of small targets

作者： Koch, W FGAN FKIE D 53343 Wachtberg Neuenahrer Strasse 20 Germany

ISBN: (纸本)0819444782

Collectively moving ground targets are typical of a military ground situation and have to be treated as separate aggregated entities. For a long-range ground surveillance application with airborne GMTI radar we in particular address the task of track maintenance for ground moving convoys consisting of a small number of individual vehicles. In the proposed approach the identity of the individual vehicles within the convoy is no longer stressed. their kinematical state vectors are rather treated as internal degrees of freedom characterizing the convoy. which is considered as a collective unit. In this context, the Expectation Maximization technique (EM), originally developed for incomplete data problems in statistical inference and first applied to tracking applications by STREIT et al. [1, 2], seems to be a promising approach. We suggest to embed the EM algorithm into a more traditional Bayesian tracking framework for dealing with false or unwanted sensor returns. the proposed distinction between 'external' and 'internal' data association conflicts (i.e. those among the convoy vehicles) should also enable the application of sequential track extraction techniques introduced by VAN KEUK [3] for aircraft formations, providing estimates of the number of the individual convoy vehicles involved. Even with sophisticated signal processing methods (STAP: Space-Time Adaptive processing). ground moving vehicles can well be masked by the sensor specific clutter notch (Doppler blinding). this physical phenomenon results in interfering fading effects, which can well last over a longer series of sensor updates and therefore will seriously affect the track quality unless properly handled. Moreover, for ground moving convoys the phenomenon of Doppler blindness often superposes the effects induced by the finite resolution capability of the sensor. In many practical cases a separate modeling of resolution phenomena for convoy targets can therefore be omitted, provided the GMTI detectio

关键词： Ground Moving targets (GMT) Expectation Maximization (EM) Bayesian/Probabilistic Multiple Hypothesis Tracking (PMHT, MHT, PDA) Minimum Detectable Velocity (MDV) Doppler blinding sensor resolution phenomena

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Comparison of two MDA algorithms for a problem in missile defense

Comparison of two MDA algorithms for a problem in missile de...

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18th conference on signal and data processing of small targets

作者： Areta, J. Bar-Shalom, Y. Levedahl, M. Univ Connecticut Storrs CT USA

ISBN: (纸本)0819462926

In this paper we compare the performance of two Multidimensional Assignment Algorithms (MDA), the Lagrangean Relaxation based S-D algorithm and the Sequential m-best 2-D algorithm, applied to a realistic problem in missile defense surveillance. the benchmark problem consists of a set of sources that provide "event" (track) estimates of multiple launches, via a number of communication networks to a Fusion Center (FC) which has to perform data association prior to fusion. the network model used "loses" the information tag that distinguishes reports from the same source transmitted through different networks, i.e., the track identity (ID) assigned by the source is not passed on. Only a track ID assigned by the network, and the source ID accompany the track. thus detection and elimination of track duplications at the FC is needed. the proposed hierarchical approach to the problem requires the solution of several MDA problems before calculating the fused estimate, so accuracy of the solution of each is crucial. Examples with several launches, sources and networks are presented to compare the performance of the two assignment algorithms.

关键词： multidimensional assignment data association benchmark

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A convex minimization approach to data association with prior constraints

A convex minimization approach to data association with prio...

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16th conference on signal and data processing of small targets

作者： Chen, HM Kirubarajan, T Univ New Orleans Dept Elect Engn New Orleans LA 70148 USA

ISBN: (纸本)081945351X

In this paper we propose a new formulation for reliably solving the measurement-to-track association problem with a priori constraints. those constraints are incorporated into the scalar objective function in a general formula. this is a key step in most target tracking problems when one has to handle the measurement origin uncertainty. Our methodology is able to formulate the measurement-to-track correspondence problem with most of the commonly used assumptions and considers target feature measurements and possibly unresolved measurements as well. the resulting constrained optimization problem deals with the whole combinatorial space of possible feature selections and measurement-to-track correspondences. To find the global optimal solution, we build a convex objective function and relax the integer constraint. the special structure of this extended problem assures its equivalence to the original one, but it can be solved optimally by efficient algorithms to avoid the combinatorial search. this approach works for any cost function with continuous second derivatives. We use a track formation example and a multisensor tracking scenario to illustrate the effectiveness of the convex programming approach.

关键词： data association multitarget tracking convex programming

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Comparison of bias removal algorithms in track-to-track association

Comparison of bias removal algorithms in track-to-track asso...

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19th conference on signal and data processing of small targets

作者： Mori, Shozo Chong, Chee BAE Syst Adv Informat Technol 5050 El Camino RealSuite 200 Los Altos CA 94022 USA

ISBN: (纸本)9780819468475

this paper compares the performance of several algorithms for estimating relative sensor biases when two sets of sensor tracks from two sensor systems are to be fused to form system tracks. the primary focus of this paper is the algorithms' performance, particularly in terms of the mean-square estimation error criterion. the efficiency of the algorithms is not our focus for this study. We are especially interested in three estimation algorithms: (1) the joint track-association/relative-bias-estimation maximum a posteriori (MAP) probability-density/probability-mass function algorithm;(2) the marginal MAP probability density estimation algorithm;and (3) the minimum-variance (MV) estimation algorithm. those algorithms rely on the capability of generating and evaluating multiple significant track-to-track association hypotheses, which may be obtained by any of the recently developed k-best bipartite data assignment algorithms. Several other algorithms that have been considered in the past will also be discussed.

关键词： multiple target tracking hierarchical data fusion track-to-track association relative bias estimation and removal

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