PRECISION PASSIVE RANGING

The problem of passive ranging is complex, yet important. This paper f ormulates it as a nonlinear least squares problem which is solved via the Newton-Raphson technique. We use the FEED method for rapid prototy ping and the automatic evaluation of partial derivatives. The paper pr esents two significant results. (1) The approach leads to rapidly conv ergent and accurate estimates of position for a variety of different n oise models. (2) The use of FEED has led to a new and exact solution t o the question of evaluating the effect of noise on parameter estimate s without the need to perform Monte Carlo computational experiments. N onlinear methods such as this require preliminary parameter estimates, for which we suggest associative memory neural networks.