2 research outputs found
Multitarget tracking using probability hypothesis density smoothing
In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. It is observed that the particle implementation of the PHD filter is dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive to missed detections and false alarms). In this paper a PHD smoothing algorithm is proposed to improve the capability of PHD-based tracking system. It involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula. Smoothing, which produces delayed estimates, results in better estimates for target states and a better estimate for the number of targets. Multiple model PHD (MMPHD) smoothing, which is an extension of the proposed technique to maneuvering targets, is also provided. Simulations are performed with the proposed method on a multitarget scenario. Simulation results confirm improved performance of the proposed algorithm. © 2011 IEEE
State Estimation and Smoothing for the Probability Hypothesis Density Filter
Tracking multiple objects is a challenging problem for an automated system,
with applications in many domains. Typically the system must be able to
represent the posterior distribution of the state of the targets, using a recursive
algorithm that takes information from noisy measurements. However, in
many important cases the number of targets is also unknown, and has also
to be estimated from data.
The Probability Hypothesis Density (PHD) filter is an effective approach
for this problem. The method uses a first-order moment approximation to
develop a recursive algorithm for the optimal Bayesian filter. The PHD
recursion can implemented in closed form in some restricted cases, and more
generally using Sequential Monte Carlo (SMC) methods. The assumptions
made in the PHD filter are appealing for computational reasons in real-time
tracking implementations. These are only justifiable when the signal to noise
ratio (SNR) of a single target is high enough that remediates the loss of
information from the approximation.
Although the original derivation of the PHD filter is based on functional
expansions of belief-mass functions, it can also be developed by exploiting elementary
constructions of Poisson processes. This thesis presents novel strategies
for improving the Sequential Monte Carlo implementation of PHD filter
using the point process approach. Firstly, we propose a post-processing state
estimation step for the PHD filter, using Markov Chain Monte Carlo methods
for mixture models. Secondly, we develop recursive Bayesian smoothing
algorithms using the approximations of the filter backwards in time. The
purpose of both strategies is to overcome the problems arising from the PHD
filter assumptions. As a motivating example, we analyze the performance of
the methods for the difficult problem of person tracking in crowded environment