Abstract
This paper deals with the problem of detection and direction of arrival (DOA) estimation of slowly moving targets against clutter in multichannel mobile passive radar. A dual cancelled channel space-time adaptive processing (STAP) scheme is proposed, aiming at reducing the system computational complexity, as well as the amount of required training data, compared to a conventional full array solution. The proposed scheme is shown to yield comparable target detection capability and DOA estimation accuracy with respect to the corresponding full array solution, despite the lower computational cost required. Moreover, it offers increased robustness against adaptivity losses, operating effectively even in the presence of a limited set of training data, as often available in the highly non-homogeneous clutter scenarios experienced in bistatic passive radar. The effectiveness of the proposed scheme and its suitability for passive GMTI are demonstrated against both simulated and experimental data collected by a DVB-T-based multichannel mobile passive radar.
Highlights
Recent advances in passive radar research have opened interesting new perspectives and application areas [1,2,3]
This is paid in terms of an increased complexity for the resulting system, since it requires (i) the estimation and inversion of a (NL × NL) space-time disturbance covariance matrix, with L being the number of temporal degrees of freedom (DOF); (ii) the availability of an amount of training data greater than 2NL in order to limit the adaptivity loss, which might be difficult to be guaranteed in the considered bistatic passive radar scenario; and (iii) the implementation of computationally expensive algorithms for the maximisation of the direction of arrival (DOA) maximum likelihood estimator (MLE) likelihood function, with computational load being dependant on the desired estimation accuracy
To mitigate the above limitations, we propose an alternative space-time adaptive processing (STAP) approach for target detection and DOA estimation in an operational mobile passive radar
Summary
Recent advances in passive radar research have opened interesting new perspectives and application areas [1,2,3]. Clutter cancellation, target detection and angular localisation, and even the spatial steering vector calibration are performed according to a fullarray strategy, i.e., by jointly exploiting all the N available channels on receive This is paid in terms of an increased complexity for the resulting system, since it requires (i) the estimation and inversion of a (NL × NL) space-time disturbance covariance matrix, with L being the number of temporal DOF; (ii) the availability of an amount of training data greater than 2NL in order to limit the adaptivity loss, which might be difficult to be guaranteed in the considered bistatic passive radar scenario; and (iii) the implementation of computationally expensive algorithms for the maximisation of the DOA MLE likelihood function, with computational load being dependant on the desired estimation accuracy. To mitigate the above limitations, we propose an alternative STAP approach for target detection and DOA estimation in an operational mobile passive radar
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