Reduced-dimension (RD) space-time adaptive processing (STAP) technique has achieved good clutter suppression performance in real data processing. However, the traditional F$A method suffers from performance degradation in presence of clutter fluctuation. Extended F$A method (E-F$A) is an effective approach to improve the performance in such a clutter environment, but it requires high computational complexity and sample support. In this paper, a novel reduced-dimension method for E-F$A clutter suppression is proposed. Firstly, we extract characteristics of clutter by tensor Tucker decomposition, which can preserve the structural characteristics of the clutter in the spatial and Doppler domains. Then, we select eigenvectors to construct the RD matrix based on principle components (PC) analysis. Finally, RD data is obtained by multiplying the RD matrix with the original data, and the weight vector for clutter suppression can be calculated. The experimental results based on real measured data validate the effectiveness of the proposed method.
In recent years, the geosynchronous satellite-based passive inverse synthetic aperture radar (ISAR) has been widely employed for target detection and automatic target recognition. However, the great distance between the satellite and the receiver causes low signal-to-noise ratio (SNR) of the target signal, which makes the system unable to obtain a well-focused image of the target. To obtain a well-focused ISAR image within a limited coherent processing interval, a method for improving the SNR is required. We propose a two-step noise suppression method to enhance the SNR by eliminating the noise components contained in the received data. The target windowing method is utilized first to reject the noise and clutter interference by windowing the target data from the range-Doppler map. Then, a denoising method is employed to further remove the noise components from the selected target data by threshold techniques. By using the two-step noise suppression, the SNR of the target signal is increased, and consequently a quality-improved ISAR image can be obtained. Simulation results validate the effectiveness of the proposed noise suppression method.
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