ang-qun Li

Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

Tracking multiple maneuvering (MM) targets is a well-known and challenging problem because of clutter and several uncertainties existing in target motion mode, target detection, and data association. An efficient solution to this problem is the Gaussian mixture probability hypothesis density (GM-PHD) filter for jump Markov system (JMS) models. However, this solution is inapplicable to circumstances where detection probability is low because the GM-PHD filter for JMS models requires a high detection probability. To address this problem, we propose a sequential multiple target (MT) Bayes filter for JMS models. To track MM targets that are switching among a set of linear Gaussian models, an implementation process of this filter for linear Gaussian jump Markov MT models is also developed. The conclusion that the novel filter is more efficient for tracking MM targets than the existing filter for JMS models in circumstances of low detection probability is validated by simulation results.

A multiple FCMs data association based algorithm for multi-target tracking

Multi-target tracking is a key problem in the field of multi-sensor data fusion. A novel algorithm of multip-target tracking based on multiple FCMs data association is proposed for the multi-sensor multi-target (MSMT) tracking system. The algorithm can make full use of the information embedded in the measurements of sensors, make the minimization of the variance of the measurement fusion value through a linear weighting, and with a great reduction of the tracking errors caused by the association errors. Then the flow diagram is presented. In a scenario having five sensors, and five targets, the simulation results show that the proposed algorithm has the advantages over the existing ones of simplicity and efficiency.

Tracking the Turn Maneuvering Target Using the Multi-Target Bayes Filter with an Adaptive Estimation of Turn Rate

Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate.

Fuzzy Quadrature Particle Filter for Maneuvering Target Tracking

Abstract In this paper, a novel fuzzy quadrature particle filter (FQPF) based on maximum entropy fuzzy clustering for maneuvering target tracking is proposed. The novelties of the fuzzy quadrature particle filter are in the update step in which the predicted and posterior probability density functions are approximated by introducing a set of quadrature point probability densities based on the Gauss–Hermite quadrature rule as a Gaussian. The particle and quadrature point weights can be adaptively estimated based on the weighting exponent and fuzzy membership degrees provided by a modified version of maximum entropy fuzzy clustering algorithm. Unlike the Gaussian particle filter (GPF) using the prior distribution as the proposal distribution, the new FQPF uses a set of modified quadrature point probability densities as the proposal distribution that can effectively enhance the diversity of samples and improve the approximate performance. Finally, simulation results are presented to demonstrate the versatility and improved performance of the fuzzy quadrature particle filter over other nonlinear filtering approaches, namely the unscented Kalman filter, quadrature Kalman filter, particle filter, and GPF, to solve maneuvering target tracking problems.

Mean shift track initiation algorithm based on Hough transform

To solve the problem of initiating tracks for multi-target in dense clutters environment, a Mean shift track initiation algorithm based on Hough transform is proposed. In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space into curves in a special parameter space; then a Mean shift clustering algorithm is executed to cluster the items gained in the parameter space, and the problem of peak seeking is also solved adaptively. Furthermore, a fuzzy influential factor, which is based on the vote number of accumulation matrix and distance between items in the parameter space and clustering center, is defined to design kernel function of Mean shift; thus clutters are removed more effectively. Experimental results show that proposed algorithm has high detection accuracy and can initiate tracks effectively.

A Novel Maneuvering Target Tracking Algorithm with Two Passive Sensors

For maneuvering target tracking with two passive sensors in clutter environment, a novel tracking algorithm based on maximum entropy fuzzy clustering is proposed. Firstly, the interacting multiple models (IMM) approach is used to solve the maneuver problem of the target, and the false alarms generated by clutter are accommodated through maximum entropy fuzzy probabilistic data association filter (MEF-PDAF). Secondly, in order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of two passive sensors is founded. Finally, the simulation results show that the proposed algorithm has the advantages over the conventional IMM-PDAF algorithm in terms of simplicity and efficiency

Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking

Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case.

A novel occlusion handling method based on particle filtering for visual tracking

Although visual tracking have been greatly improved in the last decade, there are still many challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting, are often ignored. Under the framework of particle filtering, this paper uses the incremental principal component analysis subspace method to learn an orthogonal subspace, then gets the linear representation of target appearance. To avoid the tracking drift, an occlusion handling scheme was proposed. In this scheme, firstly, determinate state of the target by the variance of the reconstruction error, then predict the position of the target by velocity prediction method and particle filtering method. The experiments revealed that proposed algorithm is impactful to deal with occlusion.

Gaussian sum quadrature particle filtering

For the nonlinear and non-Gaussian filtering problem of target tracking, a novel Gaussian sum quadrature particle filter(GSQPF) based on Gauss-Hermite quadrature and Gaussian sum particle filter is proposed. In the proposed algorithm, according to the advantage of Gaussian-Hermite quadrature points in the nonlinear approximation and the diversity of quadrature points, we introduce a set of quadrature point probability densities to approximate the important density function, the filtering and prediction densities are approximated as finite Gaussian mixtures. Because of the advantage of Gaussian mixture and the particle filtering, it can effectively improve the performance. The simulations show that the presented filter can outperform both Gaussian sum particle filter(GSPF) and quadrature particle filter(QPF).

Target tracking algorithm based on Gauss-Hermite quadrature in passive sensor array

In this paper, a new target tracking algorithm based on Gauss-Hermite quadrature is proposed in passive sensor array. Firstly, the quadrature Kalman filter (QKF) that used statistical linear regression (SLR) to linearize a nonlinear function through a set of Gauss-Hermite quadrature points is analyzed for passive target tracking. The performance of the filter is more accurate than the extended Kalman filter (EKF), the pseudo linear kalman filter (PLKF) and the unscented Kalman filter (UKF) in nonlinear dynamic system. Secondly, in order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded, and the algorithm can deal with the case of non-Gaussian noise. Finally, the simulation results show that the proposed algorithm is effective, and its performance is superiority over above methods.