354 research outputs found
Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets
AbstractThe probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches
Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking
The majority of deployed target tracking systems use some variant of the Kalman filter for their state estimation algorithm. In order for a Kalman filter to be optimal, the measurement and state equations must be linear and the process and measurement noises must be Gaussian random variables (or vectors). One problem arises when the state or measurement function becomes a multi-modal Gaussian mixture. This typically occurs with the interactive multiple model (IMM) technique and its derivatives and also with probabilistic and joint probabilistic data association (PDA/JPDA) algorithms. Another common problem in target tracking is that the target\u27s signal-to-noise ratio (SNR) at the sensor is often low. This situation is often referred to as the dim target tracking or track-before-detect (TBD) scenario. When this occurs, the probability density function (PDF) of the measurement likelihood function becomes non-Gaussian and often has a Rayleigh or Ricean distribution. In this case, a Kalman filter variant may also perform poorly. The common solution to both of these problems is the particle filter (PF). A key drawback of PF algorithms, however, is that they are computationally expensive. This dissertation, thus, concentrates on developing PF algorithms that provide comparable performance to conventional PFs but at lower particle costs and presents the following four research efforts. 1. A multirate multiple model particle filter (MRMMPF) is presented in Section-3. The MRMMPF tracks a single, high signal-to-noise-ratio, maneuvering target in clutter. It coherently accumulates measurement information over multiple scans via discrete wavelet transforms (DWT) and multirate processing. This provides the MRMMPF with a much stronger data association capability than is possible with a single scan algorithm. In addition, its particle filter nature allows it to better handle multiple modes that arise from multiple target motion models. Consequently, the MRMMPF provides substantially better root-mean-square error (RMSE) tracking performance than either a full-rate or multirate Kalman filter tracker or full-rate multiple model particle filter (MMPF) with a same particle count. 2. A full-rate multiple model particle filter for track-before-detect (MMPF-TBD) and a multirate multiple model particle filter for track-before-detect (MRMMPF-TBD) are presented in Section-4. These algorithms extend the areas mentioned above and track low SNR targets which perform small maneuvers. The MRMMPF-TBD and MMPF-TBD both use a combined probabilistic data association (PDA) and maximum likelihood (ML) approach. The MRMMPF-TBD provides equivalent RMSE performance at substantially lower particle counts than a full-rate MMPF-TBD. In addition, the MRMMPF-TBD tracks very dim constant velocity targets that the MMPF-TBD cannot. 3. An extended spatial domain multiresolutional particle filter (E-SD-MRES-PF) is developed in Section-5. The E-SD-MRES-PF modifies and extends a recently developed spatial domain multiresolutional particle filter prototype. The prototype SD-MRES-PF was only demonstrated for one update cycle. In contrast, E-SD-MRES-PF functions over multiple update cycles and provides comparable RMSE performance at a reduced particle cost under a variety of PDF scenarios. 4. Two variants of a single-target Gaussian mixture model particle filter (GMMPF) are presented in Section-6. The GMMPF models the particle cloud as a Gaussian finite mixture model (FMM). MATLAB simulations show that the GMMPF provides performance comparable to a particle filter but at a lower particle cost
Gaussian Mixture Reduction of Tracking Multiple Maneuvering Targets in Clutter
The problem of tracking multiple maneuvering targets in clutter naturally leads to a Gaussian mixture representation of the Provability Density Function (PDF) of the target state vector. State-of-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses
Information theoretic approach to robust multi-Bernoulli sensor control
A novel sensor control solution is presented, formulated within a
Multi-Bernoulli-based multi-target tracking framework. The proposed method is
especially designed for the general multi-target tracking case, where no prior
knowledge of the clutter distribution or the probability of detection profile
are available. In an information theoretic approach, our method makes use of
R\`{e}nyi divergence as the reward function to be maximized for finding the
optimal sensor control command at each step. We devise a Monte Carlo sampling
method for computation of the reward. Simulation results demonstrate successful
performance of the proposed method in a challenging scenario involving five
targets maneuvering in a relatively uncertain space with unknown
distance-dependent clutter rate and probability of detection
Joint Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications
This paper introduces a novel feedback-control based particle filter for the
solution of the filtering problem with data association uncertainty. The
particle filter is referred to as the joint probabilistic data
association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the
feedback particle filter introduced in our earlier papers. The remarkable
conclusion of our paper is that the JPDA-FPF algorithm retains the innovation
error-based feedback structure of the feedback particle filter, even with data
association uncertainty in the general nonlinear case. The theoretical results
are illustrated with the aid of two numerical example problems drawn from
multiple target tracking applications.Comment: In Proc. of the 2012 American Control Conferenc
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved
in the last decade in the context of target tracking, with regard to either a single target or multiple
targets in the presence of false or missing data. The first part of our review is on remarkable
achievements that have been made for the single-target PF from several aspects including importance
proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal
systems. The second part of our review is on analyzing the intractable challenges raised within
the general multitarget (multi-sensor) tracking due to random target birth and termination, false
alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream
multitarget PF approaches consist of two main classes, one based on M2T association approaches and
the other not such as the finite set statistics-based PF. In either case, significant challenges remain due
to unknown tracking scenarios and integrated tracking management
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