A unified spatiotemporal–geometry framework for target classification and localisation in dual-static passive radar

Abstract

Passive radar exploits ambient broadcast signals and requires no dedicated transmitter, making it attractive for covert surveillance and target monitoring. A fundamental difficulty arises at low signal-to-noise ratio (SNR) or when targets move slowly: the class decision (static vs. dynamic) and the geometry-based position estimate are solved in two independent steps by most existing methods, which can lead to inconsistent outputs. We propose a joint spatiotemporal–geometry framework for a dual-static passive radar operating on DVB-T broadcast signals at 650 MHz. The framework combines a spatiotemporal encoder with dilated convolutions and cross-attention, and a Cramér–Rao-weighted Levenberg–Marquardt bistatic solver. The two components are coupled through an iterative optimisation loop: the encoder class probability steers a physics-consistent velocity penalty inside the solver, while the updated solver state feeds back into the next class decision. Unlike prior joint methods that either operate on sequential tracks or incorporate physics only at training time, the proposed framework enforces the exact bistatic delay and Doppler equations as hard constraints at every test-time iteration while the encoder class probability actively steers the geometry penalty within the same optimisation loop. Across 500 Monte Carlo trials per SNR point and five independent evaluation seeds, the proposed method achieves a mean classification accuracy of 93.7 ± 0.8% with a weighted F1-score of 0.937 ± 0.007. The mean localisation error at −6 dB SNR is 1.15 ± 0.09 km, a 28.1% reduction compared with a geometry-only baseline. The joint optimisation converges in a mean of 4.1 ± 0.8 outer iterations. A sensitivity analysis confirms that all results are stable across a factor-of-two variation in any single hyperparameter. Within the simulated dual-static passive radar environment considered in this study, the proposed iterative approach consistently outperforms seven evaluated baseline methods in both classification accuracy and localisation error.