摘要 针对相位编码正交频分复用（Orthogonal Frequency Division Multiplexing，OFDM）雷达动目标探测问题，该文提出了一种基于通道分离和最大似然原理相结合的运动参数估计方法。首先，利用OFDM信号的正交性分离出多通道信号，并与相位编码参考信号在快时间域相关后获得各通道的1维距离像。随后，利用Keystone变换校正子载波多普勒偏移与慢时间之间的耦合，并在慢时间域和子载波域进行相参积累得到距离-多普勒2维谱。结合CLEAN技术对距离-多普勒2维谱进行谱峰搜索，获得各个目标的位置和速度参数估计量。以此为初值，利用牛顿迭代算法对似然函数进行优化，最终获得运动参数的近似最大似然估计（Approximate Maximum Likelihood Estimator，AMLE）。仿真实验表明，该文算法在计算复杂性和参数估计精度上都优于传统的Keystone估计算法，在相同均方根误差（Root-Mean-Square Error，RMSE）下其输入信噪比改善了约4 dB，且均方误差接近Cramer-Rao下限。
Abstract：To detect multiple targets on a phase-coded Orthogonal Frequency Division Multiplexing (OFDM) radar, this paper proposes a parameter estimation method based on channel separation and maximum likelihood principle. First, the multi-channel signals were separated because of the orthogonality of the OFDM system, and then the separated signals were correlated with the phase-coded reference signal in the fast time domain to acquire a 1-D range profile from each channel. Subsequently, Keystone transform was used to correct the effect of the coupling between Doppler shift of the subcarrier and the slow time domain. Simultaneously, coherent accumulation was conducted jointly in the slow time and subcarrier domains to obtain a 2-D range-Doppler spectrum. Using CLEAN technique, the peaks of this spectrum were examined to obtain the range cells and velocities of each target. Using these parameters as the initial values, the likelihood function was maximized using Newton's iterative algorithm to yield an approximate maximum likelihood estimator of the motion parameters. Simulation results demonstrate that the proposed algorithm outperforms the traditional Keystone-based estimation algorithm both in computational complexity and parameter estimation accuracy. The algorithm improved the input SNR by approximately 4 dB under the same root mean square error, and the mean square error approached the Cramer-Rao lower bound.
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2018, Vol. 7 
