面向无人机数据采集的短突发CPM盲Turbo均衡

在无人机辅助的战场侦察传感器数据采集中，短突发连续相位调制(continuous phase modulation, CPM)具有重大的实用价值，但也面临着盲均衡复杂度高、性能差等问题。基于期望最大化(expectation-maximization, EM)-维特比类盲均衡算法，针对短突发CPM提出了一种盲Turbo均衡算法。采用盲信道捕获方法设计了一组初值方案，同时在EM算法中嵌入Lazy维特比算法，提高了信道估计收敛性并降低了复杂度；接着在编码CPM中匹配卷积码和交织器，优化系统参数，并利用Turbo均衡进一步改善误码率；针对短帧迭代过程中交织深度不够导致的正反馈问题，通过改进外信息交换方式提升Turbo迭代的收敛性。理论分析和仿真结果表明，相比传统方法，所提算法在显著降低计算复杂度的同时能够兼顾误码率性能。

Blind Turbo Equalization of Short Burst CPM for UAV Data Collection

In the case of UAV-aided battlefield data collection with reconnaissance sensors, short continuous phase modulation (CPM) bursts have the problems of high complexity and poor performance for blind equalization, though with great value. Therefore, a blind Turbo equalization algorithm for short CPM bursts is proposed based on expectation-maximization Viterbi-like algorithm. Firstly, the blind channel capture method is used to design one set of initializer and the Lazy Viterbi algorithm is embedded into the expectation-maximization (EM) algorithm, which improves the convergence of channel estimation and reduces the complexity. Secondly, matched convolutional codes and interleavers are used to optimize system parameters while the Bit Error Rate (BER) performance is further improved by using Turbo equalization. Finally, aiming at the positive feedback caused by short frame, improved extrinsic information is used to improve the convergence of iteration. The theoretical analysis and simulation results show that compared with traditional methods, the proposed algorithm can significantly reduce the complexity and take into account the BER performance.