Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-no...Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.展开更多
基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系...基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系,由估计误差推算角度差值,有效降低了运算量,不需要调频斜率正负的先验信息,改进的对数搜索算法可以进一步提高参数估计结果的稳定性和可靠性。仿真结果表明,信噪比在-8 dB以上时该方法在高效率的前提下仍具有良好的参数估计性能,平均估计误差在1%以内,估计结果接近Cramer-Rao下限,满足工程实时处理需求。展开更多
为了改善线性调频(linear frequency modulation,LFM)信号脉冲压缩输出的性能,研究了误差反向传播(back propagation,BP)神经网络在线性调频信号脉冲压缩中的应用。采用遗传算法(genetic algorithm,GA)对BP神经网络的连接权值进行训练学...为了改善线性调频(linear frequency modulation,LFM)信号脉冲压缩输出的性能,研究了误差反向传播(back propagation,BP)神经网络在线性调频信号脉冲压缩中的应用。采用遗传算法(genetic algorithm,GA)对BP神经网络的连接权值进行训练学习,该算法可克服BP网络容易陷入局部最优的缺点。仿真结果表明,GA-BP网络具有较快的收敛速度和较好的数值稳定性,在信噪比损失小于1dB的条件下,可获得60dB左右的输出主旁瓣比。展开更多
为了进一步提高雷达的探测性能,设计了线性调频–二相码(LFM-M)混合调制脉冲压缩信号。采用分类比较的方法,研究了反向传播网络、Elman网络和径向基函数(RBF)网络等3种典型神经网络在其脉冲压缩中的应用,设计了网络的结构,分析了网络的...为了进一步提高雷达的探测性能,设计了线性调频–二相码(LFM-M)混合调制脉冲压缩信号。采用分类比较的方法,研究了反向传播网络、Elman网络和径向基函数(RBF)网络等3种典型神经网络在其脉冲压缩中的应用,设计了网络的结构,分析了网络的算法。通过仿真和对脉冲压缩输出性能的研究得出,采用RBF神经网络对LFM-M码信号进行脉冲压缩,网络具有较快的收敛速度和较好的数值稳定性,可获得60 d B左右的输出主旁瓣比。展开更多
基金the National Natural Science Foundation of China(Grant Nos.61973037 and 61673066).
文摘Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.
文摘为了改善线性调频(linear frequency modulation,LFM)信号脉冲压缩输出的性能,研究了误差反向传播(back propagation,BP)神经网络在线性调频信号脉冲压缩中的应用。采用遗传算法(genetic algorithm,GA)对BP神经网络的连接权值进行训练学习,该算法可克服BP网络容易陷入局部最优的缺点。仿真结果表明,GA-BP网络具有较快的收敛速度和较好的数值稳定性,在信噪比损失小于1dB的条件下,可获得60dB左右的输出主旁瓣比。
文摘为了进一步提高雷达的探测性能,设计了线性调频–二相码(LFM-M)混合调制脉冲压缩信号。采用分类比较的方法,研究了反向传播网络、Elman网络和径向基函数(RBF)网络等3种典型神经网络在其脉冲压缩中的应用,设计了网络的结构,分析了网络的算法。通过仿真和对脉冲压缩输出性能的研究得出,采用RBF神经网络对LFM-M码信号进行脉冲压缩,网络具有较快的收敛速度和较好的数值稳定性,可获得60 d B左右的输出主旁瓣比。
