自适应稀疏度正交匹配跟踪冰下水声信道估计方法
Under-ice acoustic channel estimation method based on adaptive sparsity orthogonal matching pursuit
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摘要: 根据冰下水声信道稳定的多径结构特性, 采用了利用通信帧前导信号实现信道估计的方案; 结合冰下水声信道的稀疏特性, 针对正交匹配跟踪(OMP)算法依赖稀疏度先验信息问题, 提出了自适应稀疏度正交匹配跟踪(ASOMP)算法。首先, 利用前导信号的自相关特性粗估计信道的稀疏度, 并构造简化的字典矩阵; 随后利用稀疏度粗估计值与简化的字典矩阵, 采用正交匹配跟踪算法得到信道冲激响应的粗估计结果; 最后, 基于该结果, 利用残差差分进行稀疏度和信道冲激响应的细估计。所采取的信道估计方案不需要在数据部分插入训练序列, 提高了通信速率; 所提信道估计方法不需要信道的稀疏度先验信息即可实现有效的信道估计。仿真结果表明, 所提自适应稀疏度正交匹配跟踪算法性能接近已知稀疏度的正交匹配跟踪算法, 在低信噪比下优于现有的稀疏度自适应匹配跟踪(SAMP)算法。第十四次北极科考冰下试验数据处理结果验证了所提算法的有效性和可靠性。Abstract: Based on the stable multipath structure characteristics of the under-ice acoustic channel, a scheme utilizing the preamble signal of the communication frame for channel estimation is adopted. Leveraging the sparse characteristics of the under-ice acoustic channel, an adaptive sparsity orthogonal matching pursuit (ASOMP) algorithm is proposed to address the reliance of the orthogonal matching pursuit (OMP) algorithm on prior knowledge of sparsity. Firstly, the sparsity of the channel is coarsely estimated using the autocorrelation characteristics of the preamble signal, and the simplified dictionary matrix is constructed. Subsequently, based on the coarsely estimated sparsity and the simplified dictionary matrix, the OMP algorithm is employed to obtain a preliminary estimation of the channel impulse response (CIR). Finally, fine estimations of both the sparsity and CIR are performed using residual differences derived from the coarsely estimated CIR. The adopted channel estimation scheme eliminates the need to insert training sequences into the data segment, thereby enhancing the data rate. The proposed channel estimation method enables effective channel estimation without requiring prior knowledge of channel sparsity. Simulation results show that the ASOMP algorithm achieves performance comparable to the OMP algorithm with known sparsity and outperforms the existing sparsity adaptive matching pursuit (SAMP) algorithm under low signal-to-noise ratio conditions. The data processing results from the 14th Chinese National Arctic Research Expedition’s under-ice experiment validate the effectiveness and reliability of the proposed algorithm.