Knowledge-aided Adaptive Fusion Detection Method Based on Geometric Mean Estimation
A geometric mean, knowledge-assisted technology, applied in radio wave measurement systems, instruments, etc., can solve the problems of unsatisfied assumptions and damage to the uniformity of the clutter covariance matrix structure, so as to improve the target detection performance, enhance the detection ability, The effect of improving adaptability
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Embodiment 1
[0044] Refer to the attached figure 1 , the specific implementation of embodiment 1 is divided into the following steps:
[0045] Step A1 uses the ground detection radar to irradiate the target-free range around the area to be detected, and obtain the complex amplitude of the echoes of the range units adjacent to the range unit to be detected that do not contain the target, and form K ground clutter-only Auxiliary data y k (k=1,2,...K), the auxiliary data is sent to the intermediate matrix calculation module (1); in the intermediate matrix calculation module (1), the matrix R is calculated according to formula (3) k (k=1,2,…K), and the matrix R k (k=1,2,...K) is sent to the geometric mean estimation module (2) of the clutter covariance matrix structure probability density function; in the geometric mean estimation module (2) of the clutter covariance matrix structure probability density function, Calculate the geometric mean estimate of the probability density function of t...
Embodiment 2
[0051] Refer to the attached figure 1 , the specific implementation of embodiment 2 is divided into the following steps:
[0052] Step B1 uses the sea detection radar to irradiate the target-free range around the sea area to be detected, and obtain the echo complex amplitude of the range unit adjacent to the range unit to be detected that does not contain the target, and forms K distance units containing only pure sea clutter. Auxiliary data y k (k=1,2,...K), the auxiliary data is sent to the intermediate matrix calculation module (1); in the intermediate matrix calculation module (1), the matrix R is calculated according to formula (3) k (k=1,2,…K), and the matrix R k (k=1,2,...K) is sent to the geometric mean estimation module (2) of the clutter covariance matrix structure probability density function; in the geometric mean estimation module (2) of the clutter covariance matrix structure probability density function, Calculate the geometric mean estimate of the probabilit...
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