Abstract

Matched filters are used in radar systems to identify echo signals embedded in noise. By using matched filters, range and Doppler information about the target can be extracted from the reflected signal. The use of matched filters in high frequency radars has the effect of increasing the cost and complexity of these systems. For that reason, the radar research community is looking at a new technique called compressive sensing or compressive sampling. This technique works by exploiting signal sparsity and has the potential to eliminate the use of matched filters and high frequency analog-to-digital converters. In this research, it is proposed that compressive sensing be implemented using a chaotic radar system. The goal is to eliminate the need for a matched filter and at the same time increase the target resolution. Simulation results showed that compressive sensing was capable of recovering the radar scene when stationary targets and non-stationary targets were considered. It was also observed that even when the matched filter was capable of recovering the radar scene a considerable amount of noise was introduced, making it difficult to recognize and identify the targets.

Date of publication

Spring 4-30-2012

Document Type

Thesis

Language

english

Persistent identifier

http://hdl.handle.net/10950/69

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