Passive Multi-Target Tracking Using the Adaptive Birth Intensity PHD Filter

Abstract

Passive multi-target tracking applications require the integration of multiple spatially distributed sensor measurements to distinguish true tracks from ghost tracks. A popular multi-target tracking approach for these applications is the particle filter implementation of Mahler's probability hypothesis density (PHD) filter, which jointly updates the union of all target state space estimates without requiring computationally complex measurement-to-track data association. Although this technique is attractive for implementation in computationally limited platforms, the performance benefits can be significantly overshadowed by inefficient sampling of the target birth particles over the region of interest. We propose a multi-sensor extension of the adaptive birth intensity PHD filter described in (Ristic, 2012) to achieve efficient birth particle sampling driven by online sensor measurements from multiple sensors. The proposed approach is demonstrated using distributed timedifference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements, in which we describe exact techniques for sampling from the target state space conditioned on the observations. Numerical results are presented that demonstrate the increased particle density efficiency of the proposed approach over a uniform birth particle sampler.

Multi-sensor tracking with partly overlapping FoV using detection field of probability modeling and the GLMB filter

“…However, this is not always desirable. In actual tracking scenarios, the size of TBI is usually unknown and changes over time [31][32][33]. In [34], the target birth probability is adaptively performed in the pre-processing step, combined with the current measurements to correct the preset of the target birth probability, the proposed filter can really adapt to the target birth situation and achieve better tracking accuracy.…”

Effective Passive Multitarget Localization Using Maximum Likelihood

“…Further, it is used to calculate the difference in the signal's arrival with respect to the reference sensor. Emitter devices have the benefit of providing the frequency differ-ence of arrival (FDOA) which is the result of the relative motion of the source and the target, which improves the accuracy with the estimation of the velocity of the target [2]. Passive localization of a target or multiple targets has one advantage over active localization, that passive localization is stealth in nature, which localizes the target without letting the target know about the existence of the sensors.…”