Correction sample covariance matrix estimate algorithm based on maximum posteriori

Abstract

The invention discloses a correction sample covariance matrix estimate algorithm based on the maximum posteriori; the method comprises the following steps: 1, using a MAP-GLRT detector mathematics expression to serve as a mathematics prototype; 2, using a MSCM estimate algorithm based on the maximum posteriori as a clutter covariance matrix estimate algorithm; 3, replacing the covariance matrix in the MAP-GLRT detector mathematics expression as the MSCM, thus obtaining the MAP-GLRT detector corrected mode, i.e., the MAP-GLRT MSCM detector expression of the correction sample covariance matrix estimate algorithm based on the maximum posteriori. The invention imports the MAP-GLRT MSCM detector into the correction sample covariance matrix estimate algorithm based on the maximum posteriori, thus improving the covariance matrix estimate precision; the MAP-GLRT MSCM detector has a similar detection performance with the MAP-GLRT detector when an object is detected under the uniform sea clutter background; the correction sample covariance matrix estimate algorithm can comply with real clutter environment requirements, and the detector can obtain better detection performances in actual measurement sea clutter data tests.

Description

technical field [0001] The invention belongs to the technical field of radar target detection, and in particular relates to a modified sample covariance matrix estimation algorithm based on maximum a posteriori. Background technique [0002] In sea surface target detection, it is a common technical means to adopt an adaptive target detection algorithm that matches sea clutter statistics and related characteristics. Therefore, the characteristics of the unit clutter to be detected are closely related to the design and detection performance of the adaptive detector, especially the statistical characteristics of the sea clutter will directly affect the performance of the detector. At present, in order to simplify the calculation, the adaptive detector design under the sea clutter background often uses the covariance matrix estimation algorithm to equate the statistical characteristics of the sea clutter with the statistical characteristics of the speckle, that is, the covarianc...

AI Technical Summary

Problems solved by technology

The disadvantage of this patent application is that only auxiliary data is used for covariance matrix calculation, and the data of the unit to be detected is ignored, which reduces the estimation accuracy

However, the main deficiency of this patent is: the statistical properties of sea clutter are equal to those of speckle, and the influence of texture on statistical properties of speckle is ignored, which will inevitably introduce estimation errors

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