The mainlobe jamming suppression technique is one of the key techniques of radar electronic counter- countermeasure (ECCM). In the view of multistatic radar, the interference signals received with different view angles would be highly coherent, on the contrary, the target echoes would be independent due to the radar cross section (RCS) of target would randomly fluctuate as the variation of view angle. As a consequence, After the interference signals have been aligned, a steering vector can be found of interference signals, the energy of the interference signals will be concentrated in a single-rank subspace whereas the energy of the target echo signals will be scattered throughout the whole signal space of the multistatic radar. By exploiting this phenomenon, a new mainlobe interference suppression method for multistatic radar is proposed. In this method, the projection matrix is trained based on the time domain sampling data. The interference signals received by multiple node radars with different view angles can be effectively suppressed through the noise subspace projection operation. Then, a general likelihood ratio test detector used for the residual signals after projection operation is derived. The results of simulations indicate that the proposed method can effectively suppress various kinds of mainlobe jamming and improve the detection performance. Furthermore, this method is adaptive to the variation of the interference pattern and the geometric configuration of the node array.

Feature-based target detection methods are predominantly used to determine the presence or absence of detection targets under sea clutter conditions, but they exhibit a deficiency in making nuanced classification judgments for the different categories of detected targets. Both ship targets and marine surface floating small objects display specific sea clutter characteristics to various degrees. Given the periodic consistency of transient power and the Doppler centroid bandwidth observed in sea clutter, this article examines the manifestation level of this characteristic in both categories of targets, drawing on their respective motion mechanisms. The ability to distinguish between these two categories of targets using this characteristic has been validated through the analysis of empirical data, subsequently leading to the formulation of discriminant statistics that facilitate target classification. The data confirm the effectiveness of this approach, illustrating its robust classification performance.

The synthetic aperture radar (SAR) image of moving targets will defocus due to the unknown motion parameters. For fast-maneuvering targets, the range cell migration (RCM), Doppler frequency migration and Doppler ambiguity are complex problems. As a result, focusing of fast-maneuvering targets is difficult. In this work, an efficient SAR refocusing algorithm is proposed for fast-maneuvering targets. The proposed algorithm mainly contains three steps. Firstly, the RCM is corrected using sequence reversing, matrix complex multiplication and an improved second-order RCM correction function. Secondly, a 1D scaled Fourier transform is introduced to estimate the remaining chirp rate. Thirdly, a matched filter based on the estimated chirp rate is proposed to focus the maneuvering target in the range–azimuth time domain. The proposed method is computationally efficient because it can be implemented by the fast Fourier transform (FFT), inverse FFT and non-uniform FFT. A new deramp function is proposed to further address the serious problem of Doppler ambiguity. A spurious peak recognition procedure is proposed on the basis of the cross-term analysis. Simulated and real data processing results demonstrate the validity of the proposed target focusing algorithm and spurious peak recognition procedure.

Sea surface non-stationarity poses significant challenges to sea-surface small target detection, particularly in maintaining a stable false alarm rate (FAR). In dynamic maritime scenarios with non-stationary characteristics, the non-stationarity of sea clutter can easily cause significant changes in the clutter feature space, leading to a notable deviation between the preset FAR and the measured FAR. By analyzing the temporal and spatial variations in sea clutter, we model the relationship between the preset FAR and the measured FAR as a two-parameter linear function. To address the impact of sea surface non-stationarity on FAR, the model parameters are estimated in real time within the environment and used to guide the dynamic adjustment of the decision region. We applied the proposed method to both convex hull and support vector machine (SVM) detectors and conducted experiments using measured X-band sea-detecting datasets. Experiments demonstrate that the proposed method effectively reduces the deviation between the measured mean FAR and the preset FAR. When the preset FAR is 10⁻², the proposed method achieves an average FAR of 1.067 × 10⁻² with the convex hull detector and 1.043 × 10⁻² with the SVM detector.