Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking

Abstract

In this study, the spatial distribution of false alarms is assumed to be a non-homogeneous Poisson point (NHPP) process. Then, a new method is developed under the kernel density estimation (KDE) framework to estimate the spatial intensity of false alarms for the multitarget tracking problem. In the proposed method, the false alarm spatial intensity estimation problem is decomposed into two subproblems: (i) estimating the number of false alarms in one scan and (ii) estimating the variation of the intensity function value in the measurement space. Under the NHPP assumption, the only parameter that needs to be estimated for the first subproblem is the mean of false alarm number, and the empirical mean is used here as the maximum likelihood estimate of that parameter. Then, for the second subproblem, an online multivariate local adaptive Gaussian kernel density estimator is proposed. Furthermore, the proposed estimation method is seamlessly integrated with widely used multitarget trackers, like the joint integrated probabilistic data association algorithm and the multiple hypotheses tracking algorithm. Simulation results show that the proposed KDE-based method can provide a better estimate of the false alarm spatial intensity and help the multitarget trackers yield superior performance in scenarios with spatially non-homogeneous false alarms.

Probability of detection and clutter rate estimation in target tracking systems: generalised maximum likelihood approach

“…The other important reason for the increasing interest is the novel theoretical framework in which such systems are developed. Previous algorithms were based on a more traditional Bayesian probabilistic approach, such as multiple hypothesis tracking (MHT) and All‐Neighbours Data Association, including their versions: Probabilistic Data Association (PDA) and Joint Probabilistic Data Association (JPDA) [9–12]. However, over the past ten years, the theory of random finite sets has provided a basis for the development of new tracking filters, such as probability hypothesis density (PHD) and cardinalised probability hypothesis density (CPHD) filters [7, 13, 14].…”

Joint Probabilistic Data Association Filter with Unknown Detection Probability and Clutter Rate

“…Note that the study in this paper is based on our previous work [ 19 ]: this paper refines the algorithm developed in [ 19 ] and extends the performance evaluation through extensive empirical tests. Unlike previous works, where the clutter rate estimation is decoupled from the multi-object tracking [ 14 , 20 , 21 , 22 ], the proposed approach utilises a closed feedback loop structure based on the property of the posterior of the JPDA estimations. More specifically, the multi-target JPDA filter leverages the information of estimated detection probability and clutter rate provided by the multi-Bernoulli filter.…”

Multitarget Tracking Based on PHD Smoother With Unknown Clutter Spatial Density

“…A PHD filter with clutter estimation is derived in [19] based on the Poisson point process assumption. Later on, the authors further proposed an online clutter estimation method under the Gaussian kernel density estimation framework in [20,21]. In particular, the work [22] focuses on the extended object tracking with unknown parameters in the measurement process.…”