深空组阵Eigen算法及其低信噪比合成性能分析

根据深空组阵信号模型，分析了Eigen最优合成采用的两种准则即最大输出功率和最大 信噪比准则。为了快速计算最优权值，给出了两种迭代算法即PM和PMFM算法，同时为了改善 微弱信号合成效果，在PMFM算法中引入记忆因子，提出一种可以自适应跟踪信号相位变化的 自适应Eigen算法。仿真分析了基于最大输出功率准则的Eigen合成算法和自适应Eigen算法 对低信噪比组阵接收信号的合成性能，仿真结果表明自适应Eigen算法不但具有良好的合成 效果和自适应特性而且运算量小，具有重要的工程应用价值。

Performance of Deep Space Seetion Front Eigen Combining Algorithm in Low SNR Condition

Based on deepspace arraying signal combining scheme, Eigen Algorithm built on two objectives, maximizing the combined output power and maximizing signal-to-interference plus noise ratio was analyzed. Two fast iterative algorithms, Power Method and Proposed Matrix Free Method were presented, which need smaller amount of computing operations. By adding adaptive memory factor in Proposed Matrix Free. Method, propose Adaptive Eigen Algorithm to track the signal phase shift, finally to en-hance the combining performanee in the face of weaker signals. Simulation of low received SNR signals combining performance, of all Eigen Algorithms based on maximizing the combined output power objective was given, and simulation results proved Adaptive Eigen Algorithm not only get good SNR combined loss performance and adaptive capacity, but also need less processing cost, so it is a strong candidates for practical application.