ABSTRACT Passive localisation with narrowband external illuminators is attractive for low‐cost and flexible deployment, yet accurate positioning becomes difficult when time‐delay (range) information is unreliable or unavailable. To address this limitation and leverage signals from multi‐illuminator, this paper investigates range‐free 3‐D localisation for a monostatic passive radar. Doppler and DOA measurements (and, when available, Doppler‐rate) are incorporated into a maximum‐likelihood framework for joint position–velocity estimation. To mitigate the resulting high‐dimensional optimisation, we propose an iteration based velocity estimation that expresses the velocity estimate as a function of a candidate position, reducing the original 6‐D problem to a 3‐D position search. A Gauss–Newton (GN) guided dimension‐reduced Particle Swarm Optimisation (PSO) is then employed to accelerate convergence by steering elite particles along local GN directions while preserving global exploration. Simulation and measured‐data results demonstrate that the proposed method achieves close to the Cramér‐Rao Lower Bound (CRLB) accuracy with significantly improved efficiency, enabling stable localisation with fewer observations in both single‐illuminator and multi‐illuminator configurations.
Zhao et al. (Thu,) studied this question.