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

Abstract

Abstract: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.

A Gaussian Process based Method for Multiple Model Tracking

“…Based on Bayes' rule, the posterior probability density function (PDF) of the target state vector conditioned on the measurements is, p(x 1:t |z 1:t ) ∝ p(z t |x t )p(x t |x 1:t−1 )p(x 1:t−1 |z 1:t−1 ), (5) where p(z t |x t ) = N (z t |h(x t ), R) describes the likelihood of the measurement given the state. The quantity p(x t |x 1:t−1 ) is the prediction probability which can be calculated based on the real-time motion model f t−1 (x t−1 ), i.e., p(x t |x 1:t−1 ) = N (x t |f t−1 (x t−1 ), Q).…”

“…Uncertainty can also be caused due to various parameters not being known a priori, or if they change with time. Examples of such parameters include: the turn rate when CT model is considered [5], or the process noise level. If incorrect models or parameters are applied, the tracking performance of traditional Bayesian filters would degrade and even become unacceptable.…”

“…However, in some cases, such as when the rotation is a time-varying or doubly-stochastic process, a sufficiently large set of models with different parameters is required to cover the range of possible motion models, which leads to a high computation complexity [4]. To address this aspect, [4] and [5] incorporated adaptive parameter estimation methods into the Bayesian filter framework, where the unknown parameter is estimated by approximating its distribution with particles and corresponding weights. However, as parametric models are not always able to capture all aspects of the motion behaviours, those methods might fail if the chosen motion model set is incapable of modelling the ground truth trajectory.…”

Tracking of Maneuvering Complex Extended Object with Coupled Motion Kinematics and Extension Dynamics Using Range Extent Measurements

“…In traditional radar- and sonar-based tracking applications, most target tracking approaches usually made the assumption that the received measurement originated from a point source at each time, i.e., a target is often regarded as a point source. Maneuvering target tracking has been extensively studied and well developed in many articles due to its military and civil applications, which has attracted wide attention [ 1 ]. However, with the increased resolution capability of modern sensors, an object should be regarded as extended if one target occupies more than one resolution cell or its extent is not negligible compared with the sensor resolution [ 2 , 3 ].…”