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针对无人水下航行器工作环境复杂迫切需要提高其对多目标的检测能力的问题,提出了一种采用盖尔圆半径修正峰均功率比的信息论方法(PGAIC,An Information Cri-terion using Peak-to-average Power Ratio Modified by Gerschgorin Radii).该方法首先用采样协方差矩阵的特征向量对接收数据进行加权,计算其峰均功率比,然后对采样协方差矩阵用转换矩阵进行盖尔圆变换,用盖尔圆半径修正对应的峰均功率比值,并将修正后的峰均功率比值应用于新的信息论方法得到PGAIC判源准则.仿真结果表明,PGAIC方法低信噪比下检测性能优于传统的AIC(Akaike Information Criterion)和MDL(Minimum Description Length)等方法,且不受目标强度差的影响.
Aiming at the problem of unmanned underwater vehicle’s complicated working environment and its need to improve its ability to detect multiple targets, an information theory method (PGAIC, An Information Cri-terion using Peak -to-average Power Ratio Modified by Gerschgorin Radii) .This method first weights the received data with the eigenvectors of the sampling covariance matrix, calculates the peak-to-average power ratio, and then performs a Gale-circle transform on the sampling covariance matrix using the transformation matrix , The corresponding peak-to-average power ratio is corrected by the Gale circle radius, and the modified PGA ratio is applied to the new information theory to obtain the PGAIC source criterion. Simulation results show that the PGAIC method performs better than the low signal-to- Traditional AIC (Akaike Information Criterion) and MDL (Minimum Description Length) and other methods, and not subject to the impact of poor intensity.