Probabilistic Data Association

Tracking of ground targets presents a number of challenges. Target trajectories meet various motion constraints. Substantial non-homogenous clutter is usually present. In multitarget situations measurement assignment may be computationally challenging as the number of operations increases exponentially with number of tracks and number of measurements. LMIPDA-IMM aims to provide a solution to these issues. Use of the IMM approach allows tracking ground targets with motion constraints and/or maneuvers. LMIPDA calculates the probability of target existence for false track discrimination to enable automatic track initiation and termination. The robust data association properties of LMIPDA are further enhanced by the use of a clutter map. LMIPDA provides multi-target data association with number of operations linear in the number of tracks and the number of measurements. Simulation studies illustrate the effectiveness of this approach in an environment of heavy non-homogenous clutter.

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on Time Difference of Arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multi-band Generalized Cross Correlation (GCC) analysis. The estimated directions of arrival are then post-processed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a Weighted Probabilistic Data Association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multi-band GCC, KF and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

A particle filter (PF) is a recursive numerical technique which uses random sampling to approximate the optimal solution to target tracking problems involving nonlinearities and/or non-Gaussianity. A set of particle filtering methods for tracking and manoeuvering target in clutter from angle-only measurements is presented and evaluated. The aim is to compare PFs to a well-established tracking algorithm, the IMM-PDA-EKF (interacting multiple model, probabilistic data association, extended Kalman filer), and to provide an insight into which aspects of PF design are of most importance under given conditions. Monte Carlo simulations show that the use of a resampling scheme which produces particles with distinct values offers significant improvements under almost all conditions.

The Integrated Probabilistic Data Association (IPDA) type filters provide estimates of the underlying target probability of existence /perceivability/visibility as well as track state maintenance. These quantities are conveniently used as track quality measures and can be used for track confirmation and termination. In this work five filters of this type are investigated and evaluated in a low elevation sea-surface target tracking scenario. The sea-surface induced multipath fading reduces the detection probability of the target at certain ranges which can lead to track loss. In this paper we evaluate the IPDA type filters in such scenario.

In many radar and sonar tracking systems, the target state typically includes target position and velocity components that are estimated from a time sequence of target position and Doppler measurements. The use of measured Doppler information directly in the target trajectory estimation leads to a nonlinear filter implementation such as the extended Kalman filter (EKF), particle filter etc. We investigate a method for including Doppler measurements as part of the data association process and then assess the benefits of this approach. It is well understood that data association performance can dominate the total performance of a tracker that is designed to track targets in the presence of clutter. In this case, the Doppler component of the measurements may be used in combination with the target position measurement as an additional discriminant of measurement origin. We have developed a simple but efficient Doppler data association (DDA) method which utilises both position and Doppler measurements for single and multi-target tracking. If the Doppler measurements are not used in trajectory state estimation, then the nonlinear filters for the incorporation of Doppler measurements are not required, however a significant improvement in tracking performance is still observed. The proposed DDA method is demonstrated using both the linear multi-target integrated probabilistic data association algorithm (LMIPDA) and the linear multi-target integrated track splitting algorithm (LMITS) in an active sonar underwater multi-target tracking scenario.

Joint PDA approach is very effective in tracking multiple targets in data association environment, with clutter measurements and missed detections. Joint IPDA has built upon this by including the probability of target existence as a track quality measure to enable automatic tracking and track maintenance in data association environment. Both JPDA and JIPDA suffer from the problem of track coalescence, where tracks following targets whose trajectories don't separate for extended time start following center of these targets. JPDA* is an extension of JPDA which combats coalescence by pruning possible measurement to track allocation hypotheses. Following JPDA* derivation, this paper derives JIPDA*, an extension of JIPDA which also combats track coalescence. JIPDA* updates the probability of target existence as the track quality measure. A simulation study verifies the effectiveness of this approach and compares JIPDA* with JIPDA and IPDA when tracking crossing targets in an environment of heavy clutter.

Abstract| F or the problem of tracking multiple targets the Joint Probabilistic Data Association (JPDA) approach h a s shown to be very e ective in handling clutter and missed detections. The JPDA, however, tends to coalesce neighbouring tracks and ignores the coupling between those tracks. Fitzgerald has shown that hypothesis pruning may be an e ective way t o prevent track coalescence. Unfortunately, this process leads to an undesired sensitivity to clutter and missed detections, and it does not support any coupling. To improve this situation, the paper follows a novel approach t o combine the advantages of JPDA, coupling and hypothesis pruning into new algorithms. First, the problem of multiple target tracking is embedded into one ltering for a linear descriptor system with stochastic coe cients. Next, for this descriptor system the exact Bayesian and new JPDA lters are derived. Finally, through Monte Carlo simulations, it is shown that these new PDA lters are able to handle coupling and are insensitive to track coalescence, to clutter and to missed detections.

Over the recent past, significant attention has been focused on the use of multiple sensors for target tracking over a large geographic area, as using a single sensor with a very large range is highly impractical. In addition, data from multiple sensors can be combined to provide improved estimation. Such Multi Sensor Data Fusion (MSDF) is similar to how humans and animals use multiple senses for perception. Probabilistic Data Association algorithms for Multiple Target Tracking (MTT), such as PDA and JPDA, use the statistical knowledge of the target dynamics and sensor measurement to obtain improved tracking. In this work, Probabilistic Data Association filters (i.e., both PDA and JPDA filters) for Complete Object Refinement have been designed, developed and studied. The designed PDA algorithms have been developed using a Track Oriented approach. For JPDA, association matrices are determined at each scan based on the current targets and measurements; from this the set of event matrices are formed which is observed to be the most computationally intensive part of the JPDA. A radar simulator for generating synthetic 3D target measurements along with false measurements is developed. The system has been developed using an Object Oriented Development scheme and implemented as a plug-in architecture in which new features can be added. The developed PDA and JPDA filters are extensively tested using synthetic track measurement data produced using the radar simulator. It was observed from the test results that PDA has lesser computation load in comparison to JPDA, but fails in the case of multiple interfering targets in a dense clutter. JPDA is successful in tracking such interfering targets due its joint association between targets and measurements. Due to JPDA's computation load only about 10 targets could be tracked, hence, clustering has to be used to decrease the computational load. PDA on the other hand is able to track more than 150 targets per scan. The work carried out can be used as a foundation for further development of target tracking systems with complex tracking needs.

This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses Interacting Multiple Model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using Equal-Distance Model-Set Design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.

In this letter, we compare the complexity and efficiency of several methods used for multiuser detection in a synchronous code-division multiple-access system. Various methods are discussed, including decision-feedback (DF) detection, group decision-feedback (GDF) detection, coordinate descent, quadratic programming with constraints, space-alternating generalized EM (SAGE) detection, Tabu search, a Boltzmann machine detector, semidefinite relaxation, probabilistic data association (PDA), branch and bound (BBD), and the sphere decoding (SD) method. The efficiencies of the algorithms, defined as the probability of group detection error divided by the number of floating point computations, are compared under various situations. Of particular interest is the appearance of an "efficient frontier" of algorithms, primarily composed of DF detector, GDF detector, PDA detector, the BBD optimal algorithm, and the SD method. The efficient frontier is the convex hull of algorithms as plotted on probability of error versus computational demands axes: algorithms not on this efficient frontier can be considered dominated by those that are.

The Probabilistic Data Association (PDA) method for multiuser detection in synchronous code-division multiple-access (CDMA) communication channels is extended to asynchronous CDMA, where a Kalman filter or smoother is employed to track the correlated noise arising from the outputs of a decorrelator. The estimates from the tracker, coupled with an iterative PDA, result in impressively low bit error rates. Computer simulations show that the new scheme significantly outperforms the best decision feedback detector. The algorithm has ( 3 ) complexity per time frame, where is the number of users.

In many radar or sonar tracking systems, where the state of interest typically includes target position and velocity components, target Doppler measurements may be available in addition to the target position measurements. Using measurements of Doppler information directly in target trajectory estimation leads to a nonlinear filter implementation. In this paper we investigate the methods of using Doppler measurements to improve tracking performance while maintaining the tracker implementation in the linear domain. Two new methods, namely, the one point track initialisation and the Doppler data association are proposed. By applying these two methods on the linear multi-target integrated probabilistic data association algorithm (LMIPDA), we improve tracking performance in terms of reduced number of false tracks and computational load. The merits of the proposed approaches are demonstrated via an active sonar underwater multi-target tracking scenario.

This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses Interacting Multiple Model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using Equal-Distance Model-Set Design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.

The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracking algorithm is effective in tracking Very Low Observable targets (i.e., very low signal-to-noise ratio (SNR) targets in a high false alarm environment). However, the computational complexity associated with obtaining the track estimate in many cases has precluded its use in real-time scenarios. Previous ML-PDA implementations used a multi-pass grid search to find the track estimate. In this paper, two alternate methods for finding the track estimate are presented -a genetic search and a newly developed directed subspace (DSS) search algorithm. Each algorithm is tested using active sonar scenarios in which an autonomous underwater vehicle searches for and tracks a target. Within each scenario, the problem parameters are varied to illustrate the relative performance of each search technique. Both the DSS search and the genetic algorithm are shown to be an order of magnitude more computationally efficient than the multi-pass grid search, making possible real-time implementation. In addition, the DSS search is shown to be the most effective technique at tracking a target at the lowest SNR levels -reliable tracking down to 5 dB (post-processing SNR in a resolution cell) using a 5-frame sliding window is demonstrated, this being 6 dB better than the multi-pass grid search.

In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimation by obtaining more accurate target and false alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as the Probabilistic Data Association (PDA) and Multiple Hypothesis Tracking (MHT), and results in better tracking performance of low SNR targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multiple targets for different signal to noise ratios in the framework of Finite Set Statistics (FISST). Furthermore, we illustrate how this can be incorporated into approximate multi-object filters derived from FISST, including Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters. We present simulation results for Gaussian mixture implementations of the filters that demonstrate a significant improvement in performance over just using location measurements.

A Probabilistic Data Association (PDA) method is proposed in this letter for multiuser detection over synchronous code-division multiple-access (CDMA) communication channels. PDA models the undecided user signals as binary random variables. By approximating the inter-user interference (IUI) as Gaussian noise with an appropriately elevated covariance matrix, the probability associated with each user signal is iteratively updated. Computer simulations show that the system usually converges within three to four iterations, and the resulting probability of error is very close to that of the optimal maximum-likelihood (ML) detector. Further modifications are also presented to significantly reduce the computational cost.

This paper presents sensor and data rate control algorithms for tracking maneuvering targets. The manuevering target is modeled as a jump Markov linear system. We present novel extensions of the Interacting Multiple Model (IMM), Particle filter tacker, and Probabilistic Data Association (PDA) algorithms to handle sensor and data rate control. Numerical studies illustrate the performance of these sensor and data rate control algorithms.

In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimation by obtaining more accurate target and false alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as the Probabilistic Data Association (PDA) and Multiple Hypothesis Tracking (MHT), and results in better tracking performance of low SNR targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multiple targets for different signal to noise ratios in the framework of Finite Set Statistics (FISST). Furthermore, we illustrate how this can be incorporated into approximate multi-object filters derived from FISST, including Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters. We present simulation results for Gaussian mixture implementations of the filters that demonstrate a significant improvement in performance over just using location measurements.

A two-dimensional target can be effectively and efficiently tracked from angle-only measurements only if the observing platform is sufficiently maneuvering. Covert passive sonar TMA problem is a common application, and, for that, a two-leg platform pattern is appealing both in observability and simplicity. Now, suppose communication relating to the platform's estimate of the target is intercepted by the target: Can the target therefrom infer where the (covert) platform must have been? This paper both answers the question and gives algorithms to solve it.

Modern radar systems have considerable flexibility in their modes of operation. In particular, it is possible to modify the waveform on a pulse to pulse basis, and electronically steered phased arrays can quickly point the radar beam in any feasible direction. Such flexibility calls for new methods of scheduling, both of the waveform and the beam direction so as to optimize the radar performance. We consider a radar system capable of rapid beam steering and of waveform switching. The transmit waveform is chosen from a small library. ...

This paper discusses the point process formalism of multiple target tracking problems. Finite point processes are defined as random elements in the spaces of finite sequences with their orders ignored, rather than as random integer-valued measures. Two recently developed algorithms, Joint Multitarget Probability (JMP) algorithm and Unified Data Fusion (UDF) algorithm will be described as algorithms based on the point process formalism. Two more established algorithms, Multiple Hypothesis Tracking (MHT) and Probabilistic Data Association (PDA) algorithms, will also be examined as algorithms using the point process concept. The point process formalism will then be compared with the random set formalism developed by Ron Mahler.

In this paper several turbo receivers for Interleave-Division Multiple-Access (IDMA) systems will be discussed. The multiple access system model is presented first. The optimal, Maximum A Posteriori (MAP) algorithm, is then presented. It will be shown that the use of a precoding technique at the emitter side is applicable to IDMA systems. Several low complexity Multi-User Detector (MUD), based on the Gaussian approximation, will be next discussed. It will be shown that the MUD with Probabilistic Data Association (PDA) algorithm provides faster convergence of the turbo receiver. The discussed turbo receivers will be evaluated by means of Bit Error Rate (BER) simulations and EXtrinsic Information Transfer (EXIT) charts.

A Probabilistic Data Association (PDA) method is proposed in this letter for multiuser detection over synchronous code-division multiple-access (CDMA) communication channels. PDA models the undecided user signals as binary random variables. By approximating the inter-user interference (IUI) as Gaussian noise with an appropriately elevated covariance matrix, the probability associated with each user signal is iteratively updated. Computer simulations show that the system usually converges within three to four iterations, and the resulting probability of error is very close to that of the optimal maximum-likelihood (ML) detector. Further modifications are also presented to significantly reduce the computational cost.

Approximately a decade ago the maximum likelihood probabilistic data association (MLPDA) tracking architecture was proposed; it was found, via simulation, to be a very effective (perhaps the only) way to track very low-observable contacts - down to 7-8dB (post signal processing), meaning that in a large field of view a given true contact might only rank consistently among the top five or six of those obtained. The MLPDA is based on maximizing statistical likelihood according to a precise model. Its application to real multistatic data (which is unlikely to fit MLPDA assumptions) is both a test of its effectiveness and an opportunity to explore new ways to improve it. We shall see in what follows: bistatic operation; automatic registration via the "direct blast"; the use of the Savage amplitude feature; false-alarm density modification; and a smart grid-search

The dynamic programming algorithm, called the Viterbi algorithm (VA), is an algorithm for finding the best path through the nodes of a trellis by minimizing the summed cost. It is widely used in estimation and detection problems in digital communications and signal processing. It has been also used in the speech and character recognition where the speech signals or characters are modeled by hidden Markov models. Recently, the Viterbi algorithm has been employed in data association and target tracking, namely; the Viterbi Data Association (VDA) algorithm. The target motion is assumed to be a Markov process so that the cost increment over two consecutive time instances is only dependent on the states in these instances. In this paper, two different VDA algorithms have been analyzed and compared to the standard Probabilistic Data Association (PDA) filter. Also, the performance of the algorithms is compared through simulations.

Abstract—This paper presents sensor and data rate control algo-rithms for tracking maneuvering targets. The manuevering target is modeled as a jump Markov linear system. We present novel extensions of the Interacting Multiple Model (IMM), Particle filter tacker, and Probabilistic Data Association (PDA) algorithms to handle sensor and data rate control. Numerical studies illustrate the performance of these sensor and data rate control algorithms. Index Terms—IMM algorithm, particle filters, PDA algorithm, sensor selection, target tracking.

This paper describes a multisensor single target tracking simulator “MUST” developed at CSSIP. MUST is based on a multisensor extended Kalman filter (EKF) which can handle asynchronous nonlinear multiple measurements of target parameters such as range, bearing, range rate and elevation angle. Multiple measurements from each sensor are handled by the probabilistic data association (PDA) filter. MUST provides a flexible platform where the performance of the EKF-PDA tracker can be assessed for different sensor, target and environment scenarios. This paper also presents some simulation results for different tracking scenarios and gives an initialization algorithm for the EKF-PDA algorithm

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates the PHD function, a first order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of the target states. However, the PHD filter keeps no record of target identities and hence

Knowledge area(s) Advanced (sensor-) information processing Descriptor(s) Bayesian estimation Descriptor system False measurements Formation flight Markov chain Missing measurements Multitarget tracking Stochastic hybrid system Sudden maneuvers Unresolved measurements Bayesian tracking of two possible unresolved maneuvering Bayesian tracking of two possible unresolved maneuvering Bayesian tracking of two possible unresolved maneuvering Bayesian tracking of two possible unresolved maneuvering targets targets targets targets Problem area This report studies the problem of maintaining tracks of two targets that maneuver in and out formation flight, whereas the sensor and measurement extraction chain produces false and possibly unresolved and missing measurements. If the possibility of unresolved measurements is not modeled then one of the tracks may diverge on false measurements, or the two tracks may coalesce. In order to improve this situation, during a series of studies we have developed exact and novel approximate Bayesian filtering approaches to address this problem. First, we developed a combination of a joint IMM for the joint target maneuver modes with an enhanced version of JPDA that takes coupling between target state estimates into account. We refer to this algorithm as Joint IMM Coupled PDA (JIMMCPDA). Subsequently, for this JIMMCPDA filter we developed an enhanced version which addresses track coalescence avoidance yielding the JIMMCPDA* filter, where the * stands for avoiding track coalescence. Description of work The aim of the work is to effectively enhance the IMM/JPDA paradigm to situations of possibly unresolved measurements from two targets that maneuver in and out a formation amidst false measurements. This is accomplished by combining a Gaussian shaped two-target resolution model with a descriptor system approach towards tracking multiple targets from missing and false measurements. First the considered two target track maintenance problem is defined and formulated as a problem of filtering for a jump-linear descriptor system with identically independently distributed (i.i.d.) stochastic coefficients. Next the exact Bayesian filter recursion is derived. Subsequently equations for the mode-conditional mean and covariance are developed. These equations are on their turn used for the development of the Joint interacting Multiple Model Coupled Probabilistic Data Association with Resolution (JIMMCPDAR) filter and a track-coalescence-avoiding version, which is referred to as the JIMMCPDAR*.

In tracking a single target in clutter, many algorithms have been developed ranging in complexity from nearest neighbor (NN) and probabilistic data association (PDA) to the optimal Bayesian filter. In multiple-target tracking, a number of the techniques have been exercised such as the JPDA and the multiple hypothesis (MHT) schemes. Sub-optimal algorithms, such as the PDA filter, have been used

Object detection and tracking is an essential preliminary task in event analysis systems (e.g. visual surveillance). Typically objects are extracted and tagged, forming representative tracks of their activity. Tagging is usually performed by probabilistic data association, however, in systems capturing disjoint areas it is often not possible to establish such associations, as data may have been collected at different times or in different locations. In this case, appearance matching is a valuable aid.

We present iterative turbo-like equalizers for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems subject to frequency-selective channels with rapid time variation. The proposed equalizers are obtained from a Bayesian approach based on the unbiased minimum mean-squared error (MMSE) criterion, but also admit a probabilistic data association (PDA) interpretation. Two equalizers are discussed: a basic version that makes use of symbol-level variance estimation, and an enhanced version that takes advantage of bit-level variance estimation. Simulation results show that both the presented equalizers outperform a previously proposed MMSE decision-feedback equalizer (DFE) of comparable complexity.

Most target tracking algorithms implicitly assume that target exists. There are only a few techniques that address the target existence problem along with target tracking. For example, (Integrated Probabilistic Data Association) IPDA filter addresses the target tracking and target existence problems simultaneously and it does so under at most one target assumption. In recent times random sets have been proposed as a general framework for multiple target tracking problem. However, its relationship to well understood existing tracking algorithms like IPDA has not been explored. In this paper, we show that under appropriate conditions random sets provide appropriate mathematical framework for solving the joint target existence and state estimation problem and subsequently show that it results in IPDA under appropriate simplifying assumptions.

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates the PHD function, a first order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of the target states. However, the PHD filter keeps no record of target identities and hence does not produce track valued estimates of individual targets. In this paper, we propose two different schemes according to which the PHD filter can provide track valued estimates of individual targets. Both schemes use the probabilistic dataassociation functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. In this paper, we consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. This paper also discusses the performance of the proposed schemes and compare it with that of MHT.