Application of self-organizing neural networks to multiradar data fusion

Abstract

The self-organizing neural network Dignet is used in the design of a two-stage parallel data fusion system. The data fusion is applied to target detection problems in a multichannel moving target indication (MTI) system. Features of the received data from three different radar channels are extracted via digital signal processing techniques. Pulse compression, clutter canceling, and the fast Fourier transform (FFT) are used to transform data from the time-range domain to the range-Doppler domain for feature processing. A first-stage Dignet module on each channel is used for feature clustering and prefiltering. The clustering results from each channel Dignet module are passed on to the fusion Dignet for the second-stage clustering. Map regularization, circular metric, and contrast enhancement are used to resolve the misalignment problem of features on the range-Doppler maps from sensors on different channels. Each first-stage Dignet module performs feature extraction and clustering as a filter-upon-demand module that generates adaptiveness as it is required by the data clustering. Experimental results on field data have shown that a Dignet-based multiradar data fusion system successfully detects a moving target embedded in clutter.