This paper considers the problem of multitarget tracking in cluttered environment. To reduce the dependency on the noise priori knowledge, an improved particle filtering (PF) data association approach is presented based on the
Multitarget tracking is to estimate the targets’ current positions from a series of noise-corrupted measurements by filtering methods [
Recently, the sequential Monte Carlo (SMC) data association approaches are also applied to the tracking and association problems [
The HF techniques have been used in linear-model target tracking. Accordingly, the
The remainder of this paper is organized as follows. In Section
Consider the following time-varying state-space system:
Consider the model given by (
In optimal
Consider the following discrete-time nonlinear state-space model:
In this section, the HUF based RBPF multiple target tracking algorithm (HURBPF) is provided. It can be found that the
For
End for. (Resample if needed [
It should be pointed out that in
This section presents the two-dimensional (2D) target tracking examples to demonstrate the performance of the proposed tracking algorithms.
The targets are modeled with near constant velocity model in Cartesian coordinates. The discrete-time dynamic and measurement models of the
Example of two targets tracking (a successful track by HRBPF).
The KRBPF serves as the baseline algorithm, and the proposed HRBPF algorithm is compared with it. Both algorithms are designed based on the same assumptions, and the performance of the two algorithms is evaluated by the average results over Monte Carlo runs. The initial state estimates of the two targets are set to
Comparison of the average position estimation errors of target 1.
Comparison of the average position estimation errors of target 2.
Consider a scenario of tracking two targets using bearings-only measurements received by two static sensors which are located at (
Example of two targets bearings-only tracking (a successful track by HURBPF).
The performance of the HURBPF is compared with that of the URBPF using 50 particles. The two targets begin at
The tracking performance of the HURBPF and the URBPF in terms of RMSE in position is shown in Figures
Comparison of the average position estimation errors of target 1.
Comparison of the average position estimation errors of target 2.
In this paper, we present an improved Rao-Blackwellized particle filtering algorithm by using the
The author would like to thank the anonymous reviewers for their helpful comments on this paper.