  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 time-difference-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.
