Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. In this paper, a novel method, denominated as the batch version of the extensive cancellation algorithm (ECA) in the frequency domain (ECA-FB), is presented for the first time, to suppress stationary clutter and its sidelobes. Specifically, in this method, the received signal is first divided into short batches in the frequency domain to coarsen the range resolution, and then the clutter is removed over each batch via ECA. Further, to suppress the time-varying clutter, a Doppler-shifted version of ECA-FB (ECA-FBD) is proposed. Compared with the popular ECA and ECA-B methods, the proposed ECA-FB and ECA-FBD obtained superior complex clutter suppression and slow-moving target detection performance with lower computational complexity. A series of simulation and experimental results are provided to demonstrate the validity of the proposed methods.

Owing to the continuous random and nonperiodic characteristics of the passive signal, the random range sidelobe coupling (RRSC) effects of the strong clutter (i.e., the direct path signal and the strong multipath signal) will exacerbate the performance of space-time adaptive processing (STAP) algorithm in airborne passive radar (APR). This article analyzes the influence of RRSC caused by strong clutter on detection range cell of interest and illustrates that strong clutter should be suppressed before applying the STAP algorithm. A cascaded least square (CLS) algorithm based on the directional dependence of clutter Doppler frequency is proposed for side-looking APR, where the first least square (LS) algorithm is performed to achieve the amplitude estimation of the Doppler-shift echo signal, and the channel amplitude and phase error correction factor can be obtained by the estimated amplitude information of the direct patch signal and the APR geometric configuration. The second LS is employed to further estimate the amplitude information of the strong clutter, and the resulting weight vector can be utilized for clutter cancelation. To make the CLS algorithm more practical, a batch version of the CLS (CLS-B) algorithm is proposed in this article. The advantage of the method is that it cannot only cancel clutter along the clutter ridge but also effectively prevent the suppression of targets falling in the clutter Doppler band. In addition, the proposed CLS algorithm performs well in the presence of the channel amplitude and phase error. A series of simulations are conducted to test the proposed algorithms.

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.

This article firstly analyzes the influence of utilizing the space-time adaptive processing (STAP) approaches in airborne passive radar (APR) systems on the dimension of the clutter subspace, and the signal-plus-noise ratio loss of the target, and illustrates that it is necessary to propose clutter cancellation algorithms suitable for APR systems. Combined with the property of APR systems, the multistage least squares algorithm based on subband operation (MLS-S) and the multistage least squares algorithm based on subband operation in temporal domain (MLS-ST) are proposed for the Doppler-shift clutter cancellation. The first method, in the case of the clutter with integer Doppler-shift bins, proves to be a simple solution. The second approach, at the expense of an additional stage, is suitable for more situations. Due to the use of Doppler-angle-dependence and subband operation to coarsen the range resolution, the proposed methods not only suppress effectively clutter along the clutter ridge, but also cancel the fractional-order-range clutter. In addition, the computational complexity of the proposed algorithms is low, and the clutter cancellation operations can be performed in parallel, which provide the possibility of real-time clutter suppression for APR systems. The feasibility of the proposed approaches is verified by diverse simulated scenarios.

In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, FAPBR coherent integration suffers from the problems of random range and Doppler phase fluctuations. To tackle these challenges, a novel FA signal coherent integration target detection scheme for PBR is proposed. In particular, the phase quadratic difference principle is presented for eliminating Doppler phase hopping. Then, frequency rearrangement is adopted to compensate for random range phase fluctuation while obtaining the high-range-resolution profiles (HRRPs) of the detecting target. Further, we innovatively present a sliding-range ambiguity decoupling (S-RAD) method to remove the range ambiguity effect in the case of the high pulse repetition frequency (HPRF). Compared with the existing methods, the proposed method can effectively mitigate Doppler phase hopping without requiring prior target velocity information, offering improved coherent integration performance in frequency agile signals with reduced computational complexity. Moreover, it successfully corrects the range ambiguity issue caused by HPRF. Finally, a series of simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, the possibility to improve target detection performance in passive bistatic radar by jointly exploiting multiple signals at different carrier frequencies emitted by the same illuminator is investigated, namely multi-frequency passive bistatic radar (MFPBR) coherent integration. Since the carrier frequency of each signal is agile, the MFPBR coherent integration suffers from the problems of range phase incoherence and Doppler broadening. In order to tackle these challenges, a multi-frequency coherent integration target detection algorithm for passive bistatic radar is proposed. Specifically, this scheme corrects the Doppler broadening effect via TSP. Then, low sidelobe filtering based on convex optimization is carried out to remove the range phase incoherence and obtain the MF of the target's energy. Meanwhile, the high-range-resolution profiles (HRRP) of target can be generated. The advantage of the proposed algorithm is that it can obtain superior coherent integration and detection performance for both the single and multiple targets scenarios compared to existing methods. Finally, a series of measured and simulation results are presented to demonstrate the effectiveness of the proposed algorithm.