Zong-xiang Liu

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.

Maneuvering target tracking using fuzzy logic-based recursive least squares filter

In this paper, a fuzzy logic-based recursive least squares filter (FLRLSF) is presented for maneuvering target tracking (MTT) in situations of observations with unknown random characteristics. In the proposed filter, fuzzy logic is applied in the standard recursive least squares filter (RLSF) by the design of a set of fuzzy if-then rules. Given the observation residual and the heading change in the current prediction, these rules are used to determine the magnitude of the fading factor of RLSF. The proposed filter has an advantage in which the restrictive assumptions of statistical models for process noise, measurement noise, and motion models are relaxed. Moreover, it does not need a maneuver detector when tracking a maneuvering target. The performance of FLRLSF is evaluated by using a simulation and real test experiment, and it is found to be better than those of the traditional RLSF, the fuzzy adaptive α-β filter (FAα-βF), and the hybrid Kalman filter in tracking accuracy.

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.

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.

Multi-target Bayes filter with the target detection

Abstract The probability hypothesis density (PHD) filter and marginal distribution Bayes (MDB) filter are two efficient Bayes approaches for multi-target tracking. However, these two filters fail to provide the state estimation of a target during its initial times due to the poor capability of the two filters on the target detection. To enhance the capability of the MDB filter on the target detection, we present a method for the target detection based on the rule-based track initiation technique, and develop a multi-target Bayes filter with the target detection by applying this target detection method to the MDB filter. Simulation results indicate that this filter has a stronger detecting and tracking capability of the target than the existing PHD and MDB filters.

Sequential measurement-driven multi-target Bayesian filter for nonlinear multi-target models

The sequential measurement-driven multiple target Bayesian (SMB) filter is a valid method for multiple target tracking in situation of clutter interference and detection uncertainty. The known SMB algorithm spread the marginal distribution and existence probability of objective, and sequentially handles every receiving measurements. It satisfy closed solution in linear multiple objective models. Nevertheless, the solution is inapplicable to nonlinear Gaussian multiple target models. To handle this problem, we recommended a SMB filter algorithm to adapt nonlinear Gaussian multiple objective models. The recommended implementation applies the unscented transform method to handle the nonlinearity problems. The simulation experiment conclusions show that the recommended filter is more efficient on tracking multi-targets than the traditional PHD algorithm in situation of some clutter interference.

Auxiliary truncated particle filtering with least-square method for bearings-only maneuvering target tracking

In the paper, a novel auxiliary truncated particle filtering for bearings-only maneuvering target tracking (ATPF-BOT) is proposed. In the proposed algorithm, a modified prior probability density function (PDF) is derived to solve the modeling uncertainty problem, which can simultaneously incorporate current measurement information and target characteristic information. Meanwhile, the proposal distribution is jointly designed by using the prior PDF and the modified prior PDF. Moreover, the proposal distribution is approximately calculated based on adaptive least square method so as to apply the ATPF algorithm for bearings-only maneuvering target tracking, and a practical algorithm is also developed. The experiment results show that the proposed algorithm is computationally efficient and successfully implemented in bearings-only target tracking systems.

Combinige generalized JDPA and FRLS filter for tracking multiple maneuvering targets

This paper proposes a generalized joint probabilistic (JPDA) filter for tracking multiple maneuvering targets in situations of observations with unknown random characteristics. In the proposed filter, the joint association probabilities in the standard JPDA filter are reconstructed by utilizing the generalized association probabilities of observations belonging to the targets. To calculate the generalized association probabilities, two measures of the uncertainty of statistical and fuzzy observations are defined. Using the measures, an adaptively additive fusion strategy is also proposed, which can process both statistical and fuzzy observations and keep the consistency of the estimated states with fuzzy observations. Then the fuzzy recursive least squares (FRLS) filter is adopted to update all tracks. The proposed filter has the advantage that the restrictive assumptions of statistical models for process noise and motion models are relaxed, and it does not need a maneuver detector when tracking multiple maneuvering targets. Moreover, it can adaptive adjust the weights of different types of observations in association decision according to the changes of observational environments. The performance of the proposed filter is evaluated by using the simulated data. It is found to be better than those of the traditional filters in tracking accuracy.

The sequential PHD filter for nonlinear and Gaussian models

The probability hypothesis density (PHD) filter handles the measurements periodically, once a scan period. Since measurements have to be gathered for a scan period before the PHD filter can perform a recursion, significant delay may arise if the scan period is long. To reduce this delay, we proposed sequential PHD filter. A Gaussian mixture implementation of the sequential PHD filter for nonlinear and Gaussian models is also developed, where the unscented transformation is employed to deal with the nonlinearities of target dynamic and measurement models. The simulation results demonstrate that the proposed filter updates the posterior intensity whenever a new measurement becomes available, and tracks multiple targets better than the PHD filter in the presence of missed detections.