Research On Particle Filter And Probability Hypothesis Density Filter For Target Tracking

Punctual target tracking problem is to estimate the target states, whose appearance features such as shape and color can be ignored, and provide their trajectories from a discrete set of noisy measurements in clutter. Punctual target tracking are wildly applied in military and civil fields such as location, navigation, industrial control and so on.Punctual target tracking is decomposed of single-target tracking and multiple target tracking according to the number of targets existing in the survillance region. Besides, multi-target tracking scene includes a fixed and known number of target tracking and a time-varying and unknown number of target tracking, obviously, the latter has a wilder application and is harder to resolve. Furthermore, when targets move complicatedly in clutter, it is a challenge and valuable issue to obtain their continuious trajectories from noisy measurements.This dissertation mainly addresses the issues that single target and a time-varying unknown number of targets tracking problem. In single target tracking, since data association is not necessary to consider, the key issue is the tracking algorithm, that is, filter algorithm. This dissertation uses particle filter to handle target tracking problem with nonlinear or non-Gaussian dynamic model from some classical sensors such as radar. In order to improve the performance of particle filter in single target tracking, we resolve the sample impoverishment, improve the computational efficiency and reduce the loss of variation of samples in resampling step. In the field of an unknown time-varying number of targets tracking, this dissertation focuses on the probability hypothesis density (PHD) filter and mainly addresses the issues of multi-target state estimation and track continuity. The single-target PHD decomposition is constructed in weight domain. Based on single-target PHDs from particle-PHD filter, this dissertation estimates the states of targets and identifies target trajectories by combining particle-labelling association and track management.Firstly, in single-target tracking based on particle filter,(1) Aimming at the sample impoverishment caused by rempling algorithm, this dissertation proposes a new resampling algorithm exploiting quasi-Monte Carlo (QMC) method. This algorithm generates QMC sequences around the particles with heavy weights instead of multiplying them. This scheme improves the tracking performance of particle filter from two aspects, on one hand; it solves the impoverishment of samples caused by resampling and reduces the probability of target miss-tracking. On the other hand, the estimation accuracy can be improved by taking advantage of low-discrepancy of the QMC sequence.(2) A large number of particles propogated in the iterations of particle filter result in low tracking efficiency. Based on multi-resolutional particle filter, this disserstein proposes a sample-size control method combining with filtering performance detecting. This algorithm extracts representative particles from all sets of similar particles and reduces the number of particles. Several statistic values are further defined, including quasi-measurement error, to detect whether the particle filter falls into failure. Based on these performance parameters, a sample set control algorithm for a multi-resolvational particle filter is proposd. In this scheme samples are increased if the failure has been proved to happen, which maintains the performance of filtering, and meanwhile improves the efficiency of tracking.Secondly, in particle probability hypothesis density (PHD) filter for multi-target state estimation and track continuity,(1) Particle filter implementation of the PHD filter has demonstrated a feasible suboptimal method for tracking multi-target in real-time. To obtain the target states, the peak-extraction from the posterior PHD particles needs to be implemented. This disserstein derives a decomposition of single-target PHD form. Based on it, a new state estimation method is proposed, which doesn't need to extract the PHD peaks by clustering analysis. The method provides a single-target PHD expression derived from the updated PHD equation. The target states can be directly estimated from the single-target PHD sequentially.(2) In the aspects of track continuity, PHD filter can not provide the track-valued estimates. It is a drawback of PHD filter when it is necessary to identify the individual target trajectories. State association among frames is needed to estimate the individual target trajectories. A new particle PHD filter tracker is proposed. The method extends particle-labeling association to the single-target PHD filter, associates target location estimates among time frames by combining particle-labeling association with state prediction. Furthermore, in order to filter out clutter, a new single-target PHD filter tracking model is constructed, which includes state candidate estimation, pruning, and state estimates feedback. This framework resolves the problem of multi-target tracking continuity.