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Fundamental Concepts in Radar Signal Processing

Modern radar signal processing is a broad and increasingly sophisticated field, but many of the most important techniques are based on a few fundamental concepts such as signal phase structure modeling, coherent integration, matched filtering, bandwidth-resolution relationships, maximum likelihood estimation, and modeling of interference statistics. This tutorial reviews the core techniques of pulse compression, Doppler processing, adaptive beamforming, imaging, constant false alarm rate detection, and estimation with an emphasis on highlighting their common reliance on these fundamentals.

Author Information: 

Dr. Mark A. Richards is a Principal Research Engineer and Adjunct Professor in the School of Electrical and Computer Engineering (ECE), Georgia Institute of Technology, engaged in research and teaching in the fields of radar signal processing and high performance embedded computing. Prior to joining ECE, he was Chief of the Radar Systems Division (2000-2001) and Head of the Signal Processing Branch (1995-1999) in the Sensors and Electromagnetic Applications Laboratory of the Georgia Tech Research Institute. From 1993 to 1995, he served as a Program Manager for Advanced Signal Processing in the Electronic Systems Technology Office of the Advanced Research Projects Agency (ARPA). Dr. Richards is the Editor in Chief and author or co-author of seven chapters in the text Principles of Modern Radar: Basic Concepts (SciTech Publishing, 2010), and the author of the text Fundamentals of Radar Signal Processing (McGraw-Hill, 2005). He received his Ph.D. and B.E.E. from Georgia Tech in 1982 and 1974, respectively, and his M.S.E.E. from Stanford University in 1976.

Abstract: 

Modern radar signal processing is a broad and increasingly sophisticated field, but many of the most important techniques are based on a few fundamental concepts such as signal phase structure modeling, coherent integration, matched filtering, bandwidth-resolution relationships, maximum likelihood estimation, and modeling of interference statistics. For example, pulse compression, adaptive beamforming, space-time adaptive processing, and radar imaging all are based on the basic concept of matched filtering to achieve high signal-to-interference ratio, which in turn is a form of coherent integration. Pulse compression and radar imaging additionally couple waveform design to matched filtering to simultaneously expand bandwidth and achieve fine resolution. Another example is the estimation of interference statistics in detection (constant false alarm rate processing) and space-time adaptive processing (covariance matrix estimation) using maximum likelihood estimation methods. This tutorial begins with a brief review of the impact of signal-to-interference ratio and resolution on fundamental system goals of detection, tracking, and imaging. It then reviews the important core radar signal processing methods described above to highlight their common reliance on these fundamentals.
It is assumed the student is familiar with basic concepts of signal processing such as linear filtering, Fourier transforms, and random variables, and with basic radar processing methods such as integration, Doppler filtering, and matched filtering.

Tutorial Outline: 

Introduction
o Impact of Signal-to-Interference Ratio
o Impact of Resolution

Part 1: Review of Signal Analysis Fundamentals
o Coherent Data Acquisition & Signal Phase Structure
o Coherent Integration
o Matched Filters
o Bandwidth and Resolution

Part 2: Radar Applications of Signal Modeling + Matched Filtering
o Doppler Processing
o Beamforming and STAP
o Pulse Compression
o Imaging

Part 3: Detection and Estimation
o Neyman-Pearson Detection
o Estimators
o Maximum Likelihood Estimation
o Estimating Interference Statistics
o The CRLB