This invention discloses a
clutter discrimination and suppression method based on
multiple echo features, comprising: acquiring
radar front-end
clutter and target
echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling
prior information for each connected region; using the Relief
feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal
feature vector; using this
feature vector to
train a
support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured
clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the
false alarm rate of the
system, and alleviates the computational burden of subsequent track
processing.