基于自适应网格的跳频信号参数估计

分析了现有跳频信号稀疏重构算法的基不匹配问题,导致离散字典的稀疏表示能力变差,严重影响稀疏重构算法的性能。针对这种情形,提出了基于自适应网格的变分贝叶斯稀疏重构算法。该方法通过对字典不断地加权聚类和缩放处理,实现字典的自我更新,使得参数网格更加精细化。仿真结果表明,该方法具有良好的抗噪性能和交叉项抑制能力,同时缓解了稀疏重构算法的基不匹配情形,时频聚焦性进一步提高,能够在较低信噪比条件下,获取较高时频分辨率的时频矩阵,可以更精确地完成后续跳时刻检测、跳周期及跳频率等参数估计。

Parameter estimation of frequency hopping signals based on adaptive mesh

The base mismatch problem of existing sparse reconstruction algorithms for frequency hopping signals leads to poor sparse representation ability of discrete dictionaries, which seriously affects the performance of sparse reconstruction algorithms. In view of this situation, a variational Bayesian sparse reconstruction algorithm based on adaptive mesh is proposed. The method realizes self renewal of the dictionary by continuously weighting clustering and scaling processing on the dictionary, which makes the parameter mesh more refined. The simulation results show that the proposed method has good anti-noise and cross term suppression ability. At the same time, the base mismatch of the sparse reconstruction algorithm is alleviated, and the time-frequency focusing is further improved. Under the condition of lower signal-to-noise ratio, the time-frequency matrix with higher time-frequency resolution can be obtained, and the time-hopping detection and estimation of parameters such as hopping period and frequency can be accomplished more accurately.