In a digital terrestrial multimedia broadcasting (DTMB)-based passive bistatic radar (PBR) system, the received reference signal often suffers from serious multipath effect, which decreases the detection ability of low-observable targets in urban environments. In order to improve the target detection performance, a novel reference signal purification method based on the low-rank and sparse feature is proposed in this paper. Specifically, this method firstly performs synchronization operations to the received reference signal and thus obtains the corresponding pseudo-noise (PN) sequences. Then, by innovatively exploiting the inherent low-rank structure of DTMB signals, the noise component in PN sequences is reduced. After that, a temporal correlation (TC)-based adaptive orthogonal matching pursuit (OMP) method, i.e., TC-AOMP, is performed to acquire the reliable channel estimation, whereby the previous noise-reduced PN sequences and a new halting criterion are utilized to improve channel estimation accuracy. Finally, the purification reference signal is obtained via equalization operation. The advantage of the proposed method is that it can obtain superior channel estimation performance and is more efficient compared to existing methods. Numerical and experimental results collected from the DTMB-based PBR system are presented to demonstrate the effectiveness of the proposed method.

As an evolutionary advancement to conventional synthetic aperture radar (SAR), passive bistatic SAR (PBSAR) utilizing geostationary orbit (GEO) satellite signals demonstrates significant potential for high-resolution imaging. However, PBSAR faces dual challenges in computational efficiency and phase error compensation. Traditional accelerated back-projection (BP) variants developed from monostatic SAR are incompatible with PBSAR’s geometry, and autofocus BP (AFBP) methods exhibit prohibitive computational costs and inadequate space-variant phase error handling. This study first develops a bistatic ground Cartesian back-projection (BGCBP) algorithm through subimage wavenumber spectrum correction, specifically adapted to GEO-satellite-based PBSAR. Compared to conventional BP, the BGCBP achieves an order-of-magnitude complexity reduction without resolution degradation. Building upon this foundation, we propose a subimage autofocus BGCBP (SIAF-BGCBP) methodology, synergistically integrating autofocus processing with BGCBP’s accelerated framework. SIAF-BGCBP reduces phase estimation’s complexity by 90% through subimage pixel density optimization while maintaining estimation accuracy. Further enhancement of SIAF-BGCBP via geometric inversion would enable the precise compensation of space-variant phase errors while remaining efficient. Simulations and real-environment experiments verify the effectiveness of the proposed methods.

The passive bistatic synthetic aperture radar (PBSAR) is recognized as a critical developmental direction for future radar systems. To validate its operational feasibility, we designed a PBSAR system. However, significant measurement errors were observed to degrade imaging quality. Conventional autofocusing algorithms operate under the assumption that measurement errors primarily perturb phase components while exerting negligible influence on signal envelopes. The results from the system demonstrate the invalidity of this assumption, and the performance of conventional autofocusing algorithms severely degrades under enhanced resolution requirements. To address this limitation, we propose a frequency-domain division-based multi-stage autofocusing framework. This approach improves the frequency-dependent characterization of phase errors and incorporates an image sharpness-optimized autofocusing strategy. The estimated phase errors are directly applied for signal-level compensation, yielding refocused imagery with enhanced clarity while achieving an efficiency improvement exceeding 75%. Furthermore, we introduce a ground Cartesian back projection algorithm to adapt it to the PBSAR architecture, significantly improving computational efficiency in autofocusing processing. The integration of the proposed autofocusing algorithm with the accelerated imaging framework achieves an enhancement in autofocusing performance and a computational efficiency improvement by an order of magnitude. Simulations and experimental validations confirm that the proposed methodology exhibits marked advantages in both operational efficiency and focusing performance.