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

## TL;DR

This paper introduces an adaptive multi-sensor PHD filter for passive multi-target tracking, improving particle sampling efficiency by leveraging online measurements, demonstrated with TDOA and FDOA data.

## Contribution

It extends the adaptive birth intensity PHD filter to multi-sensor scenarios, enabling more efficient target birth particle sampling based on real-time sensor data.

## Key findings

- Enhanced particle density efficiency over uniform sampling
- Effective integration of TDOA and FDOA measurements
- Improved tracking performance in computationally limited platforms

## 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 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.