Snapshot-deficient active target localization in beam-time domain using multi-frequency expectation-maximization algorithm

Abstract

The two-dimensional (2D) active target localization is generally hindered by the high temporal and spatial sidelobe levels in snapshot-deficient scenarios, where the adaptive approaches undergo performance degeneration since they require many snapshots to build the sample covariance matrix. Aiming at working robustly in snapshot-deficient active scenarios, a 2D expectation-maximization-based vertical-time-record (EMVTR) approach is proposed to compensate for the snapshot deficiency and achieve the high-resolution active localization by reconstructing the covariance matrix using estimated hyperparameters, i.e., signal powers and noise variance. With the short-time Fourier transform, the proposed approach could reduce echoes' temporal correlation and attain robust beam-time localization in mild reverberation. The multi-frequency EMVTR is derived from the single-frequency case to improve the weak echo localization. The performance is evaluated by considering single and multiple target echoes in simulation and a single moving target with tank experimental data. The results manifest the proposed EMVTR's robustness and effectiveness for the 2D active localization in snapshot-deficient scenarios.