Efficient Parameters Estimation of Multi-target Based on Space-Time Cascaded Monopulse
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摘要: 比幅单脉冲最大似然算法(ACM-ML)在进行目标参数估计时需要进行距离与速度的2维松弛迭代搜索，导致了计算效率低、运算量大的问题。针对上述问题，该文提出一种基于空时级联单脉冲的高效多目标参数估计算法(M-STCMP算法)。该算法将单脉冲概念引入脉冲域，利用时域单脉冲计算目标速度，避免了ACM-ML算法中对速度的迭代搜索，将2维松弛迭代搜索降为1维搜索，有效降低了计算复杂度。考虑时域单脉冲无法同时匹配分布在不同时域主波束的速度各异的多个检测目标，进一步利用目标信号的多普勒信息，在各多普勒单元分别进行时域单脉冲测速，并搜索目标距离值。最后为抑制目标间的信号泄露，将所有目标的估计参数进行级联迭代获得高精度参数估计值。理论分析和仿真结果验证了M-STCMP算法的有效性。Abstract: An ACM-ML (Amplitude Comparison Monopulse-Maximum Likelihood) algorithm that employs a two-dimensional relaxation iterative search for range and velocity inevitably leads to low computational speeds and large amount calculation. Focusing on the problems mentioned above, a method for efficient Multi-target parameter estimation based on a Space-Time Cascaded MonoPulse algorithm (M-STCMP) is proposed in the paper. The algorithm introduces the monopulse technique to the pulse domain for target velocity computing with temporal monopulse, which results in a one-dimensional search for range and significantly reducing the computational burden of a two-dimensional iterative search within the ACM-ML algorithm. Because the temporal monopulse cannot simultaneously match multiple targets of varying velocities across main beams, the M-STCMP algorithm is improved by estimating the velocity in each Doppler cell with the Doppler information of received signals. To suppress energy leakage between targets, estimations produced in the main beams are cascaded and iterated for each target, that results in greater accuracy. Theoretical analysis and simulation verify the effectiveness of the proposed algorithm.
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表 2 仿真2的目标参数信息
目标序号 1 2 3 速度(m/s) 11.9 41.3 13.8 多普勒单元 6 8 6 距离(m) 350.2 379.4 401.1 角度(°) 0.10 0.15 0.12 
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