Improving radar visualization system

This work develops a system to visualize the information for radar systems interfaces. It is a flexible, portable software system that allows to be used for radars that have different technologies and that is able to be adapted to the specific needs of each aplication domain in an eficient way. Replacing the visualization and processing units on existing radar platforms by this new system, a practical and inexpensive improvement is achieved.Facultad de Informátic

Radar Pulse Train Parameter Estimation and Tracking using Neural Networks

The post-deinterleaving radar pulse train problem requires estimation of the parameters and tracking of the individual pulse trains. A simple recurrent backpropagation neural network is used based on a simple state space time series formulation of the radar problem. The network incorporates a novel hueristic adaptive error threshold that allows simultaneously good tracking and parameter estimating abilities. Two simple but revealing examples are presented to show how the network is robust to missing and spurious pulses, as well as multiple level staggers with discontinuous mode changes. Introduction Multi-layered feedforward neural networks have met with considerable success when used as time series predictors for well known chaotic analytic functions as well as some "stock-market" type problems [1, 2]. Time series network predictors differ from function interpolating networks in that they involve the processing of patterns that evolve over time. If the time between network updates re..