基于相邻互相关函数-参数化中心频率-调频率分布-Keystone变换的无源雷达机动目标相参积累方法

延长积累时间可以有效提高无源雷达的目标探测能力，但是对于高速机动目标，其速度、加速度、第二加速度等因素导致现有的检测算法在积累过程中发生距离徙动(RM)和多普勒频率徙动(DFM)，使得目标检测性能恶化。该文针对无源雷达中变加速运动目标的长时间相参积累问题，提出一种基于相邻互相关函数(ACCF)-参数化中心频率-调频率分布(PCFCRD)-Keystone变换(KT)的相参积累算法(ACCF-PCFCRD-KT)。首先给出无源雷达中变加速运动目标的回波模型，分析了目标速度、加速度和第二加速度对相参积累的影响。针对目标第二加速度引起的多普勒频率弯曲，采用ACCF降低了距离和多普勒频率徙动的阶数，而后利用PCFCRD估计出目标加速度和第二加速度参数，在补偿了目标加速度和第二加速度引起的2次和3次徙动后，利用KT校正目标速度引起的线性徙动，并实现目标回波的积累。仿真结果表明，该算法可有效补偿无源雷达中目标运动导致的RM和DFM，对变加速机动目标的积累效果显著优于现有算法。

Coherent Integration Algorithm Based on Adjacent Cross Correlation Function-Parameterized Centroid Frequency-Chirp Rate Distribution -Keystone Transform for Maneuvering Target in Passive Radar

Increasing the integration time can effectively improve the detection performance of passive radar. However, for maneuvering targets, the complex motions, such as high velocity, acceleration and jerk, cause existing detection methods to suffer the Range Migration (RM) and Doppler Frequency Migration (DFM) during the integration time, which deteriorates the detection performance. This paper addresses the long time coherent integration for a maneuvering target with high-order motion (e.g., jerk motion) in passive radar systems. A method based on Adjacent Cross Correlation Function (ACCF), Parameterized Centroid Frequency-Chirp Rate Distribution (PCFCRD) and Keystone Transform (KT)(ACCF-PCFCRD-KT), is proposed. Firstly, the signal model for the maneuvering targets is given, and the influence of the target velocity, acceleration and jerk on the coherent integration is analyzed. For the Doppler curvature induced by the jerk motion, the ACCF is firstly applied to reducing the order of RM and DFM. Then the PCFCRD operation is employed to estimate the acceleration and jerk parameters. After compensating the RM and DFM caused by the acceleration and jerk, the RM arising from the velocity is corrected via the KT operation and the target echo energy is coherently integrated. Simulation results demonstrate that the proposed method can effectively compensate the RM and DFM caused by the target motion parameters in passive radar, and for a maneuvering target with jerk motion, the proposed method achieves better integration performance over the existing methods.