Registration-Based Range-Dependence Compensation for Bistatic STAP Radars
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Registration-Based Range-Dependence Compensation for Bistatic STAP Radars Fabian D. Lapierre Department of Electrical Engineering and Computer Science, University of Liege, Building B28, Sart Tilman, 4000 Liege, Belgium Email: [email protected]
Jacques G. Verly Department of Electrical Engineering and Computer Science, University of Liege, Building B28, Sart Tilman, 4000 Liege, Belgium Email: [email protected] Received 30 December 2003; Revised 18 June 2004 We address the problem of detecting slow-moving targets using space-time adaptive processing (STAP) radar. Determining the optimum weights at each range requires data snapshots at neighboring ranges. However, in virtually all configurations, snapshot statistics are range dependent, meaning that snapshots are nonstationary with respect to range. This results in poor performance. In this paper, we propose a new compensation method based on registration of clutter ridges and designed to work on a single realization of the stochastic snapshot at each range. The method has been successfully tested on simulated, stochastic snapshots. An evaluation of performance is presented. Keywords and phrases: radar, bistatic, space-time adaptive processing, range-dependence compensation, direction-Doppler curves.
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INTRODUCTION
Space-time adaptive processing (STAP) is an increasingly popular radar signal processing technique for detecting slowmoving targets in the presence of clutter and jammers [1, 2]. The space dimension arises from the use of an array of N antenna elements and the time dimension from the use of a coherent train of M pulses. The power of STAP comes from the joint processing in space and time. STAP radars operate either in monostatic configuration, where the transmitter and receiver are colocated, or in bistatic configuration, where the transmitter and receiver are located on distinct, independently moving platforms. The data collected by a STAP radar can be viewed as a sequence of M × N 2D arrays, typically treated as MN × 1 vectors. These arrays or vectors are called “snapshots.” Implementing the optimum STAP processor generally involves inverting the covariance matrix (CM) of the snapshots. This matrix must be estimated using snapshots at neighboring ranges. A major problem for virtually all STAP configurations is that the snapshots’ statistics are not stationary with respect to (w.r.t.) range. One of the most visible manifestations of this is the deformation with range of the 2D clutter power spectrum (PS), where the spectral dimensions
correspond to spatial and Doppler frequencies. Ignoring the lack of stationarity and computing the sample CM by straight averaging of single-sample CMs at neighboring ranges results in a loss of performance. The lack of stationarity of the snapshots w.r.t. range and the series of related issues are referred to in STAP as the “range-dependence (RD) problem.” Various techniques have been developed to deal with the RD problem. The main ones are Doppler warping [3, 4], angle-Doppler compensation (both deterministic
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