Abstract
The problem of passive ranging is complex, yet important. This paper formulates it as a nonlinear least squares problem which is solved via the Newton-Raphson technique. We use the FEED method for rapid prototyping and the automatic evaluation of partial derivatives. The paper presents two significant results. 1. The approach leads to rapidly convergent and accurate estimates of position for a variety of different noise models. 2. The use of FEED has led to a new and exact solution to the question of evaluating the effect of noise on parameter estimates without the need to perform Monte Carlo computational experiments. Nonlinear methods such as this require preliminary parameter estimates, for which we suggest associative memory neural networks.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
