在异构卫星网络动态组网时,为了解决星上软件通信适配器对物理层调制模式识别率低的问题,提出了一种适合低信噪比和贫先验知识的自动调制模式识别算法.该算法以高斯白噪声信道作为信道模型,选取信号高阶累积量和经典统计量作为特征参数...在异构卫星网络动态组网时,为了解决星上软件通信适配器对物理层调制模式识别率低的问题,提出了一种适合低信噪比和贫先验知识的自动调制模式识别算法.该算法以高斯白噪声信道作为信道模型,选取信号高阶累积量和经典统计量作为特征参数,采用引力搜索算法对径向基神经网络基函数中心进行优化,并在引力搜索算法中引入粒子群的信息熵来调节算法执行过程中探索与开采的关系,进一步提高了算法的分类和泛化能力.然后,利用仿真试验测评了该算法对6种卫星常用调相调制信号的识别效果.仿真试验结果表明,没有先验知识的情况下,该算法在调制信号信噪比大于4 d B时就可以达到100%的识别率,从而证明了该算法在低信噪比和贫先验知识条件下的有效性,说明算法满足星上软件通信适配器对物理层调制模式的识别要求.展开更多
In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(S...In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.展开更多
文摘在异构卫星网络动态组网时,为了解决星上软件通信适配器对物理层调制模式识别率低的问题,提出了一种适合低信噪比和贫先验知识的自动调制模式识别算法.该算法以高斯白噪声信道作为信道模型,选取信号高阶累积量和经典统计量作为特征参数,采用引力搜索算法对径向基神经网络基函数中心进行优化,并在引力搜索算法中引入粒子群的信息熵来调节算法执行过程中探索与开采的关系,进一步提高了算法的分类和泛化能力.然后,利用仿真试验测评了该算法对6种卫星常用调相调制信号的识别效果.仿真试验结果表明,没有先验知识的情况下,该算法在调制信号信噪比大于4 d B时就可以达到100%的识别率,从而证明了该算法在低信噪比和贫先验知识条件下的有效性,说明算法满足星上软件通信适配器对物理层调制模式的识别要求.
基金Projects(61471370,61401479)supported by the National Natural Science Foundation of China
文摘In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
