Effective means are urgently needed to identify non-cooperative targets intruding on airport clearance zones for the safety of low-altitude flights. Passive radars are an ideal means of low-altitude airspace surveillance for their low costs in terms of hardware and operation. However, non-ideal signals transmitted by third-party illuminators challenge feature extraction and target recognition in such radars. To tackle this problem, we propose a light-weight recognition-before-tracking method based on a beam constraint for passive radars. Under the background of sparse targets, the proposed method utilizes the continuity of target motion to identify the same target from the same array beam. Then, with its peaks detected in range-Doppler maps, a feature vector based on the biased radar cross-section is constructed for recognition. Meanwhile, to use the local scattering characteristics of targets for dynamic recognition, we introduce a parameter named normalized bistatic velocity to characterize the attitude of the target relative to the receiving station. With the proposed light-weight metric, the similarity of feature vectors between the unknown target and standard targets is measured to determine the target type. The feasibility and effectiveness of the proposed method are validated by the simulated and measured data.

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10⁻⁴, with a false detection probability of 1 × 10⁻³, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and E_b/N₀ of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of E_b/N₀ being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

Non-cooperative targets intruding on the airport clearance zones pose serious threats to the flight safety. Effective surveillance means is urgently needed to monitor and identify these threats. The passive radars are an ideal candidate for such scenarios because of their advantages in cost and operation. However, the performance of existing automatic target recognition methods designed for such radars degrades severely when there are large errors in detection and tracking. To address this problem, a plot-based method, which recognizes targets before tracking, for the bistatic passive radars is proposed in this article. We first derive a recognition feature, namely the biased radar cross section (RCS), for replacement of RCS and demonstrate the rationality of using it to characterize the scattering characteristics of targets. On this basis, we extract the biased RCS from plots detected and associated on the range-Doppler plane and build fuzzy recognizers to recognize targets. Moreover, in order to improve the recognition performance, the time fusion strategy is employed after plot recognition. Recognition results based on simulated data and measured data show encouraging performance, which validate the feasibility and effectiveness of the proposed method for recognizing non-cooperative targets before tracking.

Communication among stations of a multitstatic collaborative passive radar (MCPR) is the prerequisite for networking detection. To tackle the problems of high missed detection probability and poor carrier frequency synchronization in inter‐station communication of an MCPR under a low signal‐to‐noise ratio (SNR), we propose a virtual array‐based method to jointly detect communication signals and estimate their starting position and carrier frequency offset (CFO) at the receiving end. It takes advantage of the a priori information of the training sequence to construct SNR‐improved virtual sampled signals. On this basis, a large quantity of virtual array snapshots is constructed from the short training sequence by using the method of combinatorics, which benefits us to use the array signal processing theory in communications and reduces the signal processing cost by sharing the same hardware module with the radar signal processing unit. Moreover, to reduce the computational burden, we introduce the root multiple signal classification (root‐MUSIC) algorithm to handle the virtual array snapshots. Numerical analyses conducted on the minimum shift keying (MSK) signals validate the feasibility and effectiveness of the proposed method under low SNR.