Preprint Article Version 1 This version is not peer-reviewed

Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions

Version 1 : Received: 29 August 2019 / Approved: 2 September 2019 / Online: 2 September 2019 (02:54:19 CEST)

How to cite: Cooke, C. Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions. Preprints 2019, 2019090015 (doi: 10.20944/preprints201909.0015.v1). Cooke, C. Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions. Preprints 2019, 2019090015 (doi: 10.20944/preprints201909.0015.v1).

Abstract

Bello's stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth's surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date, no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a wideband generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

Subject Areas

airborne radar; radar clutter; radar signal processing; stochastic systems; time-varying systems; maximum entropy

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