基于神经网络的ML方向估计
THE ML BEARING ESTIMATION BY USE OF NEURAL NETWORKS
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摘要: 本文提出一种用于最大似然(ML)方向估计的神经网络模型。理论分析和模拟结果表明,这种网络一般可以在电路的时常数数量级内给出目标方向的ML估计值,而且网络结构和参数固定,阵列阵元输入直接作为网络的输入而无需任何运算。因此这种网络非常适用于实时处理。这为实时实现目标的精确定位提供了一条新途径。Abstract: A neural network to implement the maximum likelihood bearing estimation algorithm in real time is proposed. Both analysis and simulation show that this neural network is guaranteed to be stable and to provide the maximum likelihood bearing estimation within an elapsed time of only a few characteristic time constants of the network. As a result, this proposed neural network is satisfactory for real time bearing estimation.
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I. Ziskind, M. Wax, IEEE Trans. on ASSP, ASSP-36(1988)4,1553-1560.[2]R. O. Schmidt, IEEE Trans. on AP, AP-34(1986)3, 276-280.[3]M. Kaveh, A. J. Baraleu, IEEE Trans.on ASSP, ASSP-34(1986)2, 331-341.[4]M. P. Kennedy, L. O. Chua, IEEE Trans. on CAS, CAS-34(1987)2, 210-214.[5]M. P. Kennedy, L. O. Chua, IEEE Trans. on CAS, CAS-35(1988)5, 554-562.[6]R. Rastogi et al., Array Signal Processing with Interconnected Neuron-like Elements, ICASSP, Texas, (1987), pp. 2328-2331.[7]S. K. Chapman, T. S. Durral, Bearing Estimation Using Neural Networks, ICASSP, New York, (1988), pp.1483-1486. -
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