A neural network approach to MVDR beamforming problem

A Hopfield-type neural network approach which leads to an analog circuit for implementing the real-time adaptive antenna array is presented. An optimal array pattern can be steered by updating the weights across the array in order to maximize the output signal-to-noise ratio (SNR). The problem of adjusting the array weights can be characterized as a constrained quadratic nonlinear programming. The adjustment of settings is required to respond to a rapid time-varying environment. A Hopfield-type neural net with a number of graded-response neurons designed to perform the constrained quadratic nonlinear programming would lead to a solution in a time determined by RC time constants, not by algorithmic time complexity. The constrained quadratic programming neural net has associated it with an energy function which the net always seeks to minimize. A fourth-order Runge-Kutta simulation shows that the circuit operates at a much higher speed than conventional techniques and the computation time of solving a linear array of 10 elements is about 0.1 ns for RC=5×10 ^-9