Target maneuver detection using a particle filter with spawn model and particle labeling

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on random finite sets. It propagates the PHD function, the first order moment of the posterior multi-target density, from which the number of targets as well as their individual states can be extracted. Furthermore, the sequential Monte Carlo (SMC) approximation of the PHD filter (also known as particle-PHD filter) is available in the literature in order to overcome its intractability. However, the PHD filter keeps no track of the target identities and hence cannot produce track-valued estimates of individual targets. This work consider the use of an improved implementation, of the particle-PHD filter that gives the track-valued estimates of individual targets and propose a novel way for doing so. The improved PHD filter combines the particles approximation of the posterior PHD function and the peak extraction from the posterior PHD particles to create the target identities of the individual estimates. The improved PHD filter does not affect the convergence results of the particle-PHD filter

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination

We propose an approach for tracking a varying number of simultaneously active acoustic wideband signal sources in an acoustic enclosure. Relying on the assumption of W-disjoint orthogonality, the method uses narrowband position estimates of the sources for the targets. The instantaneous position estimates form clusters rather than single points, as would be required for a conventional Probability Hypothesis Density (PHD) filter. Therefore, we model the position estimates as extended targets and use a special form of the PHD filter, the extended target Gaussian mixture PHD filter, for tracking the targets. This allows to model target birth and death, which correspond to speech onset and end of utterance, respectively. With this model and by using the well-developed theory of Finite Set Statistics (FISST)-based multi-target tracking, we provide a comprehensive, strictly Bayesian treatment of the problem of tracking wideband acoustic sources using narrowband position estimates. We validate the results by tracking a varying number of targets in an enclosure simulated with the image-source method.

The probability hypothesis density (PHD) filter has emerged as a promising tool for dealing with the multi-target tracking problem in recent years. However, except in some special situations, closed-form recursive update equations for the PHD filter do not exist and the particle filter approaches have to be used. The output of the particle filter at each step is the particle clouds approximation of the PHD. Thus, some special algorithms are needed to extract the target states from those particles. Utilising the information of both particles’ weight and their spatial distribution, an improved algorithm named C-Clean is proposed in this study. This algorithm is comprised of two steps. First, clustering techniques are used to exploit the spatial distribution of particles. Then, within those clusters whose corresponding PHD weight is beyond some predefined threshold, the peak extraction procedure modified from the CLEAN technique is taken to extract the multi-target state. Simulation results demonstrate that its performance is better than those algorithms using the information of particles’ spatial distribution or weight only.

We evaluate the use of a probability hypothesis density (PHD) filter in a bearings-only tracking application. The main feature of a PHD filter is that it propagates the first-order statistical moment of a multisource posterior distribution. Multisource estimation using a PHD filter has been shown to reliably track multiple simulated targets in the bearings-only case. In this paper we evaluate the utility of the sequential Monte-Carlo PHD filter for tracking surface ships using bearings-only data acquired from a Bluefin-21 unmanned underwater vehicle in Boston Harbor. The unmanned underwater vehicle was equipped with a rigidly mounted planar hydrophone array that measures the bearing angle to sources of acoustic noise, of which shipping traffic is the dominant source. We further evaluate several target maneuvering models, including clockwise and counter-clockwise coordinated turns. The combination of the coordinated turn models with a constant velocity model is used in a multiple model PHD filter. The results of the multiple model PHD filter are compared to the results of a PHD filter using only a constant velocity model.

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

This paper explores application of the Probability Hypothesis Density (PHD) filter to the estimation of a threat level pertaining to an object population. Specifically, it develops explicit and compact expression for computation of its variance, a second-order statistical moment that quantifies the dispersion of the threat level around its mean value. The behaviour of the statistic is demonstrated through simulation examples.

The Probability Hypothesis Density (PHD) filter is a computationally tractable alternative to the optimal nonlinear filter. The PHD filter propagates the first moment instead of the full posterior density. Evaluation of the PHD enables one to extract the number of targets as well as their individual states from noisy data with data association uncertainties. Recently, a smoothing algorithm was proposed by the authors to improve the capability of PHD based tracking. Smoothing produces delayed estimates, which yield better estimates not only for the target states but also for the unknown number of targets. However, in the case of the maneuvering target tracking problem, this single model method may not provide accurate estimates. In this paper, a multiple model PHD smoothing method is proposed to improve the tracking of multiple maneuvering targets. A fast sequential Monte Carlo implementation for a special case is also provided. Simulations are performed with the proposed method consisting of multiple maneuvering targets. Simulation results confirm the improved performance of the proposed algorithm.

In recent years there has been much interest in the probability hypothesis density (PHD) filtering approach, an attractive alternative to tracking unknown numbers of targets and their states in the presence of data association uncertainty, clutter, noise, and miss-detection. In particular, it has been discovered that the PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and birth. This finding opens up a new direction where the PHD filter can be practically implemented in an effective and reliable fashion. However, the previous work is not general enough to handle jump Markov systems (JMS), a popular approach to modeling maneuvering targets. In this paper, a closed form solution for the PHD filter with linear JMS is derived. Our simulations demonstrate that the proposed PHD filtering algorithm provides promising performance. In particular, the algorithm is capable of tracking multiple maneuvering targets that cross each other.

We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and removes noise and clutter. Moreover, this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. In order to account for the nature of the PHD propagation, we propose a novel particle resampling strategy and we adapt dynamic and observation models to cope with varying object scales. The proposed resampling strategy allows us to use the PHD filter when a priori knowledge of the scene is not available. Moreover, the dynamic and observation models are not limited to the PHD filter and can be applied to any Bayesian tracker that can handle state-dependent variances. Extensive experimental results on a large video surveillance dataset using a standard evaluation protocol show that the proposed filtering framework improves the accuracy of the tracker, especially in cluttered scenes.

This paper is focus on multiple target tracking with arbitrary clutter process by using the probability hypothesis density (PHD) filter. Based on the Random Finite Set (RFS) framework, the clutter process in the classical PHD filter is modeled as Poisson RFS, which is only reasonable for some scenarios in reality. The PHD filter suitable for arbitrary clutter process has not yet been implemented. In this paper, an arbitrary clutter PHD filter, as well as its particle filter implementation, are derived by using the probability generating functional (PGFL) based on the RFS framework. Simulation results show that the proposed arbitrary clutter PHD filter can be used for multiple target tracking with arbitrary clutter process.

Particle filters for probability hypothesis density filter with the presence of unknown measurement noise covariance

Asynchronism among the local radars is one of the most important challenges for multi-target tracking (MTT) in multi-static radar systems. In order to address this challenge in the framework of random finite set (RFS) based on Bayesian inference, we propose a decentralized probability hypothesis density (PHD) filter based on the asynchronous periodical sequential estimation (APSE). First, starting from a multi-target Bayesian filter, we derive the multi-target density update expressions for the APSE solution in the RFS framework. Next, we develop the PHD recursion expressions of the APSE solution, named as APSE-PHD, and describe the Gaussian mixture (GM) implementation of the APSE-PHD. Simulation results for a challenging tracking scenario confirm that the proposed APSE-PHD algorithm is effective for MTT in the asynchronous multistatic radar system and outperforms the existing PHD-based algorithm.

The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem. We show that, under reasonable assumptions, a sequential Monte Carlo (SMC) approximation of the PHD filter converges in mean of order \(p \geq 1\), and hence almost surely, to the true PHD filter. We also present a central limit theorem for the SMC approximation, show that the variance is finite under similar assumptions and establish a recursion for the asymptotic variance. This provides a theoretical justification for this implementation of a tractable multiple-object filtering methodology and generalises some results from sequential Monte Carlo theory.