对于高速自旋目标而言,为了获得聚焦效果良好的图像,通常要求雷达系统具有较高的脉冲重复频率(PRF)。当PRF不满足采样要求时,雷达接收到的通常是方位欠采样的回波数据,从而影响目标识别。本文根据压缩感知理论,结合自旋目标回波信号稀疏性的特点,建立方位欠采样的成像模型,并提出了基于压缩采样匹配追踪的窄带雷达高速自旋目标成像算法。该算法在对信号进行重构迭代中,采用回退策略,并结合回波信号稀疏性的具体参数,选取最优的支撑集,提高了算法的重构质量。仿真实验表明新算法在回波欠采样的情况下能很好地重构图像,尤其是在低信噪比的情况下。采用"误选数"和"均方误差"两个指标对CoSaMP算法重建信号的性能进行评价,结果表明该算法受雷达PRF和信噪比SNR的影响较小,算法稳定性好。

High-speed spinning targets dection and imaging is essential to some special applicaions, such as targets classification and recognition, missile defense, the protection of spacecraft, etc. A higher pulse repetition frequency (PRF) is required for the radar system to obtain focused images for high-speed spinning targets. However, the observations of targets are always undersampled or nonuniformly-sampled when the radar PRF cannot satisfy the sampling requirement, which influences target identification. To solve the problem, this paper establishes an imaging model of azimuth undersampled echoes, according to the compressed sensing (CS) theory and the sparsity nature of ISAR echoes. Then the compressive sampling matching pursuit (CoSaMP) algorithm is used for signal reconstruction to improve the stability of traditional imaging algorithm using OMP.
The overall procedures of the proposed algorithm are as follows. (1) After range compression and rough translational motion compensation of the echoes, a range bin containing the target component is determined due to the usage of narrowband radar. (2) The spinning period and residual translational motion parameters are estimated using the time-frequency spectrum of the echoes. (3) The noise level is estimated using the range bins containing only noise. The observation matrix is constructed according to the estimated target motion parameters. The narrowband imaging of spinning targets is transformed into an optimization problem based on CS. (4) The target signal is reconstructed using the CoSaMP algorithm, which is then transformed into the target space. Then the two-dimensional images are obtained.
The first experiment is provided to show the effectiveness of the proposed algorithm. Compared with imaging algorithm based on OMP，the algorithm improves the reconstruction accuracy and stability due to the usage of backtracking strategy. The imaging quality is highly decided by two factors, the PRF and the signal-to-noise ratio (SNR). In the second experiment, the influences of these two factors on the imaging performance using different imaging algorithms are investigated. Two indicators, including the number of false selections and normalized mean-square error, are introduced to evaluate the influences of RRF and SNR on the imaging performance. The results about these two indicators show that compared to the OMP and the SP algorithms, the proposed algorithm can reconstruct the target signal more effective and stable, especially under the conditions of low SNR and PRF. The shadowing effect, which results from the scatterers of the target that will be unsighted to radar in some observation intervals during a spin cycle, is considered in the third experiment. It well demonstrates the ability of the proposed algorithm to alleviate the shadowing effect. The locations of all the scatterers can be correctly estimated from the seriously insufficient samples.
When the radar PRF cannot satisfy the Nyquist sampling theorem, the CoSaMP algorithm is used for image formation of high-speed spinning targets based on the CS theory. The algorithm further improves the ability of information-access of low PRF radar on the high-speed spinning targets, which benefits the target recognition.