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Approaches to Radar Signal Processing

IP.com Disclosure Number: IPCOM000131616D
Original Publication Date: 1983-Jun-01
Included in the Prior Art Database: 2005-Nov-11
Document File: 15 page(s) / 50K

Publishing Venue

Software Patent Institute

Related People

William C. Booth: AUTHOR [+3]

Abstract

Separating desirable from undesirable signals requires a number of signal processing techniques'. For greater accuracy and resolution, most of today's systems use digital computations. The goal of modern radar signal processing is to improve the accuracy and dependability of detection and measurement in radar systems. In addition to the desired signals, radar systems receive interference and noise signals, many of which can be quite strong. The receiver and associated elements also generate noise. Signals that are desirable in one system may interfere in another. For example, the return from a rain storm is a desired signal in weather radar but an annoyance in defense system tracking radar. A number of signal processing techniques have been developed over the years to separate desired signals from undesired signals in radar raw video.~~3 Many of these techniques were implemented initially in the form of analog circuits, but as digital circuits became faster, a growing number of these functions were implemented in digital form. In addition, many new techniques have been developed that are possible only with digital processors.

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THIS DOCUMENT IS AN APPROXIMATE REPRESENTATION OF THE ORIGINAL.

This record contains textual material that is copyright ©; 1983 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Contact the IEEE Computer Society http://www.computer.org/ (714-821-8380) for copies of the complete work that was the source of this textual material and for all use beyond that as a record from the SPI Database.

Approaches to Radar Signal Processing

William C. Booth

Signal Processing Systems

Separating desirable from undesirable signals requires a number of signal processing techniques'. For greater accuracy and resolution, most of today's systems use digital computations.

The goal of modern radar signal processing is to improve the accuracy and dependability of detection and measurement in radar systems. In addition to the desired signals, radar systems receive interference and noise signals, many of which can be quite strong. The receiver and associated elements also generate noise. Signals that are desirable in one system may interfere in another. For example, the return from a rain storm is a desired signal in weather radar but an annoyance in defense system tracking radar.

A number of signal processing techniques have been developed over the years to separate desired signals from undesired signals in radar raw video.~~3 Many of these techniques were implemented initially in the form of analog circuits, but as digital circuits became faster, a growing number of these functions were implemented in digital form. In addition, many new techniques have been developed that are possible only with digital processors.

Signal processing techniques

One of the earliest techniques developed was thresholding, a selection process wherein all signal elements below a threshold level are rejected and only signals above the threshold are displayed or processed further. Thresholding is very useful in clearing out unwanted signals so that only strong targets are displayed or processed. However, thresholding does not improve the accuracy of the system or help to separate a weak target from interfering signals.

For separating weak signals from strong noise and/or interfering signals, variations of two techniques are used: integration and Doppler separation. Integration techniques improve the separation of noiselike interfering sources from the desired signal (plus noise). They are based on the fact that the average value of signal plus noise is greater~an the average value of the noise. We refer here to postdetection integration, which is integration of the signal after the magnitude (or magnitude squared) of each sample has been determined. This technique works best for signals that are greater than the average value of noise but are masked by the instantaneous variations in noise level. By adding several (or many) successiveireturns, the signals can be drawn out of the surrounding noise. This effect is illustrated in Figure 1. The proce...