Knowledge-aided transmit signal and receive filter design in signal-dependent clutter

Knowledge-aided transmit signal and receive filter design in signal-dependent clutter

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In this chapter, we consider the problem of knowledge-aided transmit signal and receive filter design for point-like target in signal-dependent clutter. We suppose that the radar system has access to a (potentially dynamic) database containing a geographical information system, characterizing the terrain to be illuminated, and some a priori electromagnetic reflectivity and spectral clutter models, allowing the raw prediction of the actual scattering environment. Hence, we devise an optimization procedure for the transmit signal and the receive filter that sequentially improves the signal-to-interference-plus-noise ratio (SINR). Each iteration of the algorithm, whose convergence is analytically proved, requires the solution of both a convex and a hidden convex optimization problem. The resulting computational complexity is linear with the number of iterations and polynomial with the receive filter length. At the analysis stage, we assess the performance of the proposed technique in the presence of either a homogeneous ground clutter scenario or a heterogeneous mixed land and sea clutter environment.

Inspec keywords: convex programming; iterative methods; convergence of numerical methods; radar signal processing; knowledge based systems; telecommunication computing; geographic information systems; computational complexity; filtering theory; radar clutter

Other keywords: geographical information system; spectral clutter model; dynamic database; point like target; convex optimization problem; computational complexity; mixed land-sea clutter environment; raw prediction; knowledge aided transmit signal design; signal-to-interference-plus-noise ratio; actual scattering environment; receive filter design; iterative algorithm; signal dependent clutter; a priori electromagnetic reflectivity; radar system

Subjects: Communications computing; Optimisation techniques; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Signal processing theory; Expert systems and other AI software and techniques; Radar equipment, systems and applications; Geography and cartography computing; Optimisation techniques; Filtering methods in signal processing

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