A Parametric Approach to Space-Time Adaptive Processing in Bistatic Radar Systems
Artikel i vetenskaplig tidskrift, 2022

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in environments of clutter and jammers. Effective STAP implementations are dependent on an accurate estimate of the space-time covariance matrix, which characterizes noise and interference in the radar signal. Inside-looking monostatic radar systems, the estimate based on secondary radar observations is rather straight forward as all the samples in secondary data can be argued to be from a single distribution, and the sample covariance can be used as an estimate of the space-time covariance matrix. However, in many other radar configurations, the vital underlying STAP training assumption that secondary data are identically distributed is violated, which implies that detection performance can be significantly degraded. This article develops a new method that can be used when secondary data do not share a common distribution due to geometry-induced range dependencies. This phenomenon is of particular concern in bistatic radar systems. We propose a model-based approach, where the distribution of noise and clutter for each range bin is parameterized by a set of scenario parameters. Using secondary data, the scenario parameters are estimated by maximizing the likelihood function. Based on the estimated scenario parameters, the STAP covariance estimate is formed for the cell under test. The presented method is compared with other state-of-the-art methods for bistatic radar STAP via numerical simulations. The simulations indicate that the presented method, with a proper initialization, yields an estimate of the STAP covariance matrix that significantly increases the signal-to-interference-plus-noise ratio compared to the other investigated methods.

Covariance matrices

model-based ground clutter mitigation


Bistatic radar systems

Bistatic radar


Maximum likelihood estimation



space-time adaptive processing (STAP)


Jacob Klintberg

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Patrik Dammert

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik


IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN) 15579603 (eISSN)

Vol. 58 2 1149-1160

Passiv och multistatisk radar

VINNOVA (2017-04864), 2017-11-10 -- 2021-12-31.


Sannolikhetsteori och statistik





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