Clutter suppression method based on knowledge-assisted sparse iterative covariance estimation

A technology of covariance estimation and knowledge assistance, applied in the field of radar, it can solve the problems of complexity, large amount of calculation, increase of calculation amount, etc., and achieve the effect of suppressing complex and strong ground clutter, good real-time performance, and small calculation amount of estimation

Active Publication Date: 2019-01-01
XIDIAN UNIV
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Problems solved by technology

The rank reduction method (RR) makes full use of the distribution characteristics of the clutter in the echo, selects a complete clutter space to form an adaptive weight, cancels the clutter component, and improves the clutter suppression performance, which mainly includes the principal component (PC) method, cross-spectrum method (CSM) and multi-level Wiener filter (MWF), but whether it is PC, CSM or MWF, it is necessary to estimate the dimension of the clutter subspace in advance, and to find the reduced-rank transformation matrix and determine the cl

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  • Clutter suppression method based on knowledge-assisted sparse iterative covariance estimation
  • Clutter suppression method based on knowledge-assisted sparse iterative covariance estimation
  • Clutter suppression method based on knowledge-assisted sparse iterative covariance estimation

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Embodiment 1

[0028] Traditional STAP needs to select the echo data near the unit to be detected as a training sample to estimate it. The selected training sample and the data of the unit to be detected must satisfy the independent and identical distribution conditions. However, in fact, airborne radar usually works in non-uniform noise. In the wave environment, the IID assumption is difficult to satisfy in the actual environment, resulting in a significant decrease in the clutter suppression performance of STAP. In view of the above non-uniformity problems, various methods of suppressing non-uniform clutter have appeared, and the traditional STAP has been improved. The common ones are the rank reduction method and the structuring method. The accuracy of clutter suppression is improved, but the computational load and complexity are also increased. Therefore, the improvement of traditional STAP can effectively suppress ground clutter, significantly improve the detection performance of weak a...

Embodiment 2

[0048] The clutter suppression method based on knowledge-assisted sparse iterative covariance estimation is the same as that of Embodiment 1, and the construction of the first intermediate variable D and the second intermediate variable ρ(i) described in step 2 is as follows:

[0049] is the initial estimated clutter power matrix Iterates continuously to correct the clutter power matrix to make the final estimate To make it closer to the true value, it is necessary to construct two intermediate variables for the iterative process, and construct the value of the first intermediate variable D(Nc×1), whose expression is:

[0050]

[0051] Among them, a(f s,m ) means that the normalized spatial frequency is f s,m The spatial steering vector of the corresponding mth clutter block, the superscript H represents the complex conjugate operation, m∈{1,2,…,Nc}, Nc represents the number of independent clutter blocks on the equidistant ring, and tr represents the matrix trace opera...

Embodiment 3

[0057] The clutter suppression method based on knowledge-assisted sparse iterative covariance estimation is the same as that of Embodiment 1-2, and the lth unit data x to be detected after the ith iteration described in step 3 l Clutter power matrix on a clutter ridge Specifically:

[0058] After the i-th iteration, the l-th unit data x to be detected l The power of the mth clutter block on the clutter ridge Its expression is:

[0059]

[0060] Among them, a(f s,m ) means that the normalized spatial frequency is f s,m the spatial steering vector of the corresponding mth clutter block, Represents the spatial covariance matrix of the lth unit data to be detected reconstructed after the ith iteration, () -1 Represents the matrix inversion operation, and the superscript H represents the conjugate transpose operation.

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Abstract

The invention discloses a clutter suppression method based on knowledge-assisted sparse iterative covariance estimation, and solves the problem of poor clutter suppression performance of the conventional space-time adaptive processing technology because of non-uniformity of the clutter environment. The implementation steps are listed as follows: computing an airborne radar space-time steering vector matrix; determining an initial clutter power matrix and constructing an intermediate variable; computing a clutter power matrix in iteration by means of the intermediate variable; performing iteration to obtain the final clutter power matrix; determining a space-time covariance matrix constructed by the data of one unit to be detected and the corresponding weight; and traversing all the units to be detected so as to obtain the space-time adaptive processing result. The clutter covariance matrix is reconstructed by using the data of the units to be detected so as to avoid non-uniformity of the training samples, effectively suppress high ground clutters and improve the detection performance of the slow moving target; and the computational burden is low, the real-time performance is betterand engineering implementation is easy so that the clutter suppression method is suitable for the airborne radar to suppress the high ground clutters in the non-uniform environment and detect the slow ground moving target.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to airborne radar clutter suppression, in particular to a clutter suppression method based on knowledge-assisted sparse iterative covariance estimation, which is suitable for airborne early warning radar to suppress strong ground clutter in non-uniform environment As well as detecting slow moving targets on the ground. Background technique [0002] For airborne early warning radars, the beams are generally directed horizontally or slightly downward. At this time, the radar inevitably observes many strong ground clutter. Complex and strong ground clutter seriously affects the detection of ground moving targets by airborne early warning radar. Therefore, the ground clutter must be suppressed first to detect moving targets under the background of complex and strong ground clutter. Space-time adaptive processing technology (STAP) can simultaneously distinguish moving targets ...

Claims

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Application Information

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IPC IPC(8): G01S7/36G01S7/41
CPCG01S7/36G01S7/414
Inventor 王彤乔格阁肖浩王美凤
Owner XIDIAN UNIV
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