Parametric Estimation Techniques for Space-Time Adaptive Processing with Applications for Airborne Bistatic Radar Systems
In this thesis, we study a parametric scenario based approach to alleviate the geometry-induced effects. Thus, the considered framework is based on so called radar scenarios. A radar scenario is a description of the current state of the bistatic configuration, and is thus dependent on a few parameters connected to the two radar platforms which comprise the configuration. The scenario description can via a parametric model be used to represent the geometry-induced effects present in the system. In the first topic of this thesis, an investigation is conducted of the effects on scenario parameter residuals on the performance of a detector. Moreover, two methods are presented which estimate unknown scenario parameters from secondary observations. In the first estimation method, a maximum likelihood estimate is calculated for the scenario parameters using the most recent set of secondary data. In the second estimation method, a density is formed by combination of the likelihood associated with the most recent set of radar observations with a prior density obtained by propagation of previously considered scenario parameter estimates through a dynamical model of the scenario platforms motion over time. From the formed density a maximum a posteriori estimate of the scenario parameters can be derived. Thus, in the second estimation method, the radar scenario is tracked over time. Consequently, in the first topic of the thesis, the sensitivity between scenario parameters and detector performance is evaluated in various aspects, and two methods are investigated to estimate unknown scenario parameters from different radar scenarios.
In the second part of the thesis, the scenario description is used to estimate a space-time covariance matrix and to derive a generalized likelihood ratio test for the airborne bistatic radar configuration. Consequently, for the covariance matrix estimate, the scenario description is used to derive a transformation matrix framework which aims to limit the non-stationary behavior of the secondary data observed by a bistatic radar system. Using the scenario based transformation framework, a set of non-stationary secondary data can be transformed to become more stationarily distributed after the transformation. A transformed set of secondary data can then be used in a conventional estimator to estimate the space-time covariance matrix. Furthermore, as the scenario description provides a representation of the geometry-induced effects in a bistatic configuration, the scenario description can be used to incorporate these effects into the design of a detector. Thus, a generalized likelihood ratio test is derived for an airborne bistatic radar configuration. Moreover, the presented detector is adaptive towards the strength of both the clutter interference and the thermal noise.
Parametric Space-Time Adaptive Processing
Airborne Bistatic Radar Systems
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
A Parametric Approach to Space-Time Adaptive Processing in Bistatic Radar Systems
IEEE Transactions on Aerospace and Electronic Systems,; Vol. 58(2022)p. 1149-1160
Artikel i vetenskaplig tidskrift
A Parametric Generalized Likelihood Ratio Test for Airborne Bistatic Radar Systems
2022 IEEE RADAR CONFERENCE (RADARCONF'22),; (2022)
Paper i proceeding
Mitigation of Ground Clutter in Airborne Bistatic Radar Systems
Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop,; Vol. 2020-June(2020)
Paper i proceeding
Passiv och multistatisk radar
VINNOVA (2017-04864), 2017-11-10 -- 2021-12-31.
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5259
Opponent: Braham Himed, Air Force Research Laboratory, USA