Abstract
Space-time adaptive processing (STAP) techniques have been motivated as a key enabling technology for advanced airborne radar applications. In this paper, a slow-time code design is considered for the STAP technique in airborne radar, and the principle for improving signal-to-clutter and noise ratio (SCNR) based on slow-time coding is given. We present two algorithms for the optimization of transmitted codes under the energy constraint on a predefined area of spatial-frequency and Doppler-frequency plane. The proposed algorithms are constructed based on convex optimization (CVX) and alternating direction (AD), respectively. Several criteria regarding parameter selection are also given for the optimization process. Numerical examples show the feasibility and effectiveness of the proposed methods.
Highlights
Traditional space-time adaptive processing (STAP) involves multi-dimensional adaptive filtering which combines signals from several antenna elements and from multiple pulse repetitions to suppress clutter, interference, and noise in both space and time
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In [12,13], joint design problems of transmitting waveforms and the receiving filter were studied in terms of signal-toclutter and noise ratio (SCNR) maximization and a manifold-based alternating optimization (MAO) method and an iterative algorithm based on the minorize-maximization (MM)
Summary
Traditional space-time adaptive processing (STAP) involves multi-dimensional adaptive filtering which combines signals from several antenna elements and from multiple pulse repetitions to suppress clutter, interference, and noise in both space and time. The suboptimal solution of the phase-coded waveform for detecting a particular target in additive noise was proposed in [6]. Proposed average and worst-case performance metrics and several algorithms to solve highly nonconvex design problems. It should be pointed out that waveform design problem for STAP has been discussed from the viewpoint of fast-time coding. In literature [10], the joint design of receive filter and waveform for the waveform adaptive radar STAP problem was solved by an alternating minimization algorithm. In [12,13], joint design problems of transmitting waveforms and the receiving filter were studied in terms of SCNR maximization and a manifold-based alternating optimization (MAO) method and an iterative algorithm based on the minorize-maximization (MM). Diag(.) denotes forming a diagonal matrix whose diagonal entries are formed by a vector, whereas diag(·) denotes the vector formed by collecting the diagonal entries of the matrix, E{·} stands for the statistical expectation operator, < is positive semidefinite, is positive-definite, and k.k F indicates the Frobenius norm
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