目标的振动会对雷达回波产生特殊的相位调制，称为微多普勒效应，能够提供对微动目标检测的有利信息，因此对提高SAR(Synthetic Aperture Radar)系统性能具有重要意义。然而，已有的检测算法存在运算量大、抗杂波噪声能力弱和无法适应多振动目标等问题。针对这些问题，本文提出一种对振动目标检测的新算法。该算法利用相位中心天线偏置DPCA(Displaced Phase Center Antenna )对消技术消除杂波，并沿方位向累加DPCA信号来提高算法的抗噪声能力。由于振动目标SAR方位回波的频谱与脉冲序列具有高度相似性，本文算法选择了检测重复脉冲序列的脉冲重复频率PRI(Pulse Repetition Interval )变换法来实现振动目标的检测。仿真实验表明，本文算法能够在强杂波噪声条件下检测振动目标，同时具有准确振动频率估计性能，甚至当同一个单元存在多个振动目标时，本文算法依然适用。仿真中振动目标检测的计算机运行总时间不超过0.6 s，说明本文算法适用于实时检测，通过与GLRT算法和Hough变换算法运算量的比较，证明了本文算法相比于经典算法具有运算量小，检测速度快的优点。

Target vibration generates special phase modulation of Synthetic Aperture Radar (SAR) echo signals called micro-Doppler effect, which can provide favorable information for micro-motion target detection and recognition. Mining such favorable information is of great significance to improve the performance of SAR systems. At present, research on SAR vibration target detection is still insufficient. The existing detection algorithms have many problems, such as large computational complexity, weak anti-clutter and anti-noise ability, and inability to adapt to multiple vibration targets.To solve SAR real-time detection of multiple vibration targets under a strong clutter and noise background, this paper proposes a novel vibration target detection algorithm. The detection algorithm uses the Displaced Phase Center Antenna (DPCA) cancellation technique to suppress ground clutter and accumulates DPCA signal along the azimuth direction to improve the anti-noise ability. The frequency spectrum of the SAR azimuth echo of a vibrating target has a high similarity with pulse sequence. Therefore, the pulse repetition interval transform (PRI transform) method of detecting repeated pulse sequences is chosen in this paper to realize the detection of vibration targets. The detection algorithm is a two-step process. The first step is to find the range cell positions of the vibration targets (called the aim range cells). This step mainly uses DPCA technology to eliminate clutter and accumulates the DPCA signal along the azimuth direction. Then, the vibration target range cells are determined by setting an appropriate threshold. The second step is to determine the number of vibration targets in the aim range cells obtained in the first step and estimate their vibration frequencies. This step mainly performs pseudo-pulse processing on the azimuth spectrum of the target range cell and then performs PRI transformation on the pseudo-pulse train to detect the vibration targets and estimate the vibration frequencies. The algorithm can detect the vibration targets under strong clutter and noise conditions, and it has high frequency estimation precision and a small amount of calculation. Even when multiple vibration targets are present in a single range cell, the algorithm is still applicable.Simulation results prove the correctness and high efficiency of the proposed algorithm. Results show that under the condition of weak Signal-to-Noise Ratio (SNR), the first step of the detection can determine the vibration target range cells well, and when the SNR>-40 dB, the detection probability of the aim range cell is higher than 95%. In the second step, all the vibration targets in each aim range cell are detected successfully, and the vibration frequency of each vibration target is estimated accurately. Compared with the GLRT algorithm and the Hough transform algorithm, the proposed algorithm has the advantages of small computational complexity and high detection speed. The total computation time of two-step detection does not exceed 0.6 s, which shows that the proposed algorithm is suitable for real-time detection. The quasidata detection results prove that detecting vibration targets in the actual scene is feasible.The algorithm outperforms previous ones in that it involves a fairly small amount of computation and exhibits better anti-clutter and anti-noise performance. Hence, this algorithm has high practical value for remote sensing vibration targets.