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当星载SAR(synthetic aperture radar)原始数据的饱和度较高时,传统BAQ(block adaptive quantization)算法性能明显下降.为了改善其性能,本文研究了原始文献中采样信号幅度均值与输入ADC(analog to digital converter)信号标准差的映射关系,指出了其结果的疏漏并给出推导过程.在引入ADC输出信号标准差概念的基础上,本文完整地推导了采样信号幅度均值与ADC输出信号标准差的映射关系. Monte-Carlo实验表明,在饱和度全集上,以上两种映射均非SNR(signal to noise ratio)意义下的最优映射.因此,本文提出分段线性映射的概念以及饱和度全集最优搜索算法.对于线性映射段,给出了理论证明和k值解析解;对于非线性映射段,给出了分段线性映射的k值搜索算法.仿真数据及实测数据实验结果表明,当SAR原始数据饱和时,本文提出算法的数据域SNR优于传统BAQ算法的数据域SNR.
When the saturation of the original data of synthetic aperture radar (SAR) is high, the performance of the traditional block-adaptive quantization (BAQ) algorithm obviously decreases.In order to improve its performance, the paper analyzes the relationship between the mean value of the sampled signal and the input ADC to digital converter, pointing out the omission of the result and giving the derivation process.On the basis of introducing the concept of standard deviation of ADC output signal, this paper completely deduces the standard deviation between the average amplitude of sampled signal and the output signal of ADC Monte-Carlo experiments show that the above two kinds of mappings are not optimal SNR in the sense of signal-to-noise ratio (SNR). Therefore, this paper proposes the concept of piecewise linear mapping and the complete set of saturation The optimal search algorithm is proposed.For the linear mapping segment, the theoretical proof and k-value analytic solution are given.For the nonlinear mapping segment, the k-valued search algorithm for piecewise linear mapping is given.The simulation results and the experimental results show that when When the original SAR data is saturated, the SNR of the data field of the proposed algorithm is better than that of the traditional BAQ algorithm.