Biologically Inspired Target Recognition in Radar Sensor Networks

Qilian Liang

doi:10.1155/2010/523435

Abstract

One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC). Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE) or soft-max approaches. In this paper, we apply these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT) is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2D construction algorithm does not work in this study.

Highlights

Introduction and MotivationHumans display a remarkable capability to perform visual and auditory information integration despite noisy sensory signals and conflicting inputs
How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC)
We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT

Summary

Introduction and Motivation

Humans display a remarkable capability to perform visual and auditory information integration despite noisy sensory signals and conflicting inputs. Unlike medical imaging or synthetic aperture radar imaging where abundance of data is generally available through multiple looks and where processing time may not be crucial, practical cognitive radar sensor networks are typically the opposite: availability of data is limited and required processing time is short. This need is motivated by the fact that humans display a remarkable capability to quickly perform target recognition despite noisy sensory signals and conflicting inputs.

Human Information Integration Mechanisms

Radar Sensor Networks Data Measurement and Collection

Human-Inspired Sense-through-Foliage Target Detection

Fuzzy Logic System for Automatic Target Detection

Conclusions