A modified Lagrange programming neural network(LPNN)iterative algorithm based on maximum likelihood estimation was proposed for solving nonlinear equations in the field of passive time difference of arrival(TDOA)localization.The cost function based on maximum likelihood estimation was established,and the general constrained optimization problem of TDOA equations in combination with space-time constraints was constructed.In addition,the convergence and asymptotic stability of the network were proven through the iterative algorithm.Simulation verification and performance analysis of two commonly used array element placement methods in the field of TDOA localization were conducted.The results of simulation experiments show that the algorithm can provide accurate coordinate estimation with an error less than 1.414 x 10-3.Compared to conventional algorithms,this method has better performance in various noise environments,with a mean square error of 0.786 6 in 0 dB noise environments.
passive localizationTDOA localizationtime difference of arrivalmaximum likelihood estimationLagrange programming neural networkanalog neural networkgeneral constrained optimization problemcost function
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