中文摘要：

对含有系统误差的测量进行配准是准确进行数据关联的前提.实际中,许多不确定性因素导致系统误差,使其演化模型难以建立,从而导致传统配准方法不再适用.为此,提出一种基于优化SA-PSO（simulated annealing particle swarm optimization）的配准算法.由于传感器监视空域经常受到杂波的影响,在利用SA-PSO优化算法对系统误差进行配准时,不仅要考虑外界因素所引发系统误差的不确定性问题,还要考虑目标多个量测的归属问题.基于此,提出一种联合改进退火粒子群优化和概率数据关联的算法SA-PSO-PDA（simulated annealing and particle swarm optimization and probability data association）,它综合考虑系统误差的随机性、寻优的最佳化和目标量测的多样性.仿真结果表明了所提算法具有可行性,且能较好地寻优系统误差参数.

英文摘要：

The registration was the prerequisite to precise association of detection data with system bia- ses. In practice, it was difficult to model the system biases caused by many uncertain factors, thus, the traditional registration methods were not adopted to solve sensor registration. An optimization method based on SA-PSO （simulated annealing particle swarm optimization） was presented. However, the clut- ters could affect surveillance task, and it was necessary to consider the uncertain system biases and multi- ple measurement echo simultaneously. Therefore, a novel registration approach named SA-PSO-PDA （simulated annealing and particle swarm optimization and probability data association） was proposed. This approach considered random system biases, optimal evolution and various measurements. Simulation results showed that the proposed method was feasible, and obtains optimal system biases parameters were better than in other methods.