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Abstract:   (42 Views)

In this paper, the algorithms for adaptive rejection of a radar clutter are synthesized for the case of a priori unknown spectral-correlation characteristics at wobbulation of a repetition period of the radar signal. The synthesis of required algorithms for the non-recursive adaptive rejection filter (ARF) of a given order, which is used in modern radar systems, reduces to determination of the weighting coefficients vector, which realizes the best effectiveness index for radar signal extraction from the moving targets on the received clutter background. As the effectiveness criterion, we consider the averaged (over the Doppler signal phase shift) improvement coefficient for a signal-to-clutter ratio (SCR), which is usually used at describing the modern radars. On the base of extreme properties of the characteristic numbers (eigennumbers) of the matrices, the optimal vector is defined as the eigenvector of the correlation matrix of the clutter, corresponding to its minimal eigenvalue. After transition to the estimate value of the clutter correlation matrix according to the adaptive Bayesian approach, the general type of the vector of optimal ARF weighting coefficients is determined.  The specific adaptive algorithms, depending upon the ARF order, are obtained, which in the particular cases can be reduced to the known algorithms confirming its authenticity. The comparative analysis of the synthesized and known algorithms is performed. It is shown that adaptation of offered processing algorithms in combination with its time adjustment leads to significant benefits in clutter rejection effectiveness compared to the known processing algorithms. Application of the algorithms obtained during ARF designing allows increase the effectiveness of radar signal extraction from the moving targets in much more range of its radial velocities on the clutter background in the case of unknown spectral-correlation properties.

Type of Study: Research Paper | Subject: Radar and Sonar
Received: 2016/08/5 | Accepted: 2017/07/20 | Published: 2017/09/6