基于CS的Kalman滤波OFDM信道估计

在OFDM系统信道估计中,准确的时域卡尔曼滤波（TDKF）估计需要信道多径时延作为先验条件,而且具有较低的频谱效率.考虑到大多数无线信道具有稀疏和时变的特性,提出一种改进的卡尔曼滤波与压缩感知联合信道估计方法,采用稀疏度自适应匹配追踪（SAMP）算法,并对信道响应变化量进行重建.仿真结果表明,相较于已有算法,提出的算法不需要知道信道的稀疏度,而且信道估计结果更加准确.

An improved channel estimation method for OFDM systems based on Kalman filtering and CS

The application of accurate time-domain Kalman filtering （TDKF） channel estimation in orthogonal frequency division multiplexing （OFDM） requires of channel tap locations as a prior knowledge, which also have lower spectral efficiency. Considering the sparseness and time-variance of most wireless channels, the proposed method which combines TDKF and compressive sensing（CS） uses sparsity adaptive matching pursuit （SAMP） algorithm and takes CS to Kalman innovation. The simulation results have demonstrated that, compared with the current approach, the method proposed can achieve an improved accuracy without knowing the channel sparsity.