A clutter distribution identification method based on the AD (Anderson-Darling) goodness-of-fit test is proposed for the environmental perception and clutter distribution identification of ground radar positions, given the current demand for environment perception by position adaptive technology. The method determines which distribution the clutter obeys by comparing the critical value of the corresponding distribution with the AD statistic. The AD test method is improved by proposing a cell averaging method and a critical value unitisation method to address the problem that the AD parameters of the parameters in ground radar echo sample data fluctuate greatly, and the sample data are accepted by multiple distributions. The measured ground radar echo data was recorded and used for identification. The analysis results show that the improved AD test identification method has a better identification effect in the identification of ground radar’s position environment clutter distribution.
A K-Means clustering algorithm modified MTI (Moving Target Indication) filter is proposed for refining signal processing of the array environment in response to the current demand for clutter suppression by array adaptive technology. The method first uses the clutter distribution type and distance as parameters of the K-Means clustering algorithm to divide the echo’s into different clutter distance segments. Then an adaptive MTI filter is used to filter each clutter distance segment separately. The identification was carried out using measured ground radar echo data. The results show that the k-means clustering algorithm has better clutter distance segmentation capability. Compared with the conventional two-pulse MTI filter, the improved segmented MTI filter of the k-means algorithm has better clutter suppression in complex clutter environments.
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