A Radar Target Track Initiation Method Based on Random Forest
A technology of random forest and track initiation, applied in computer parts, reflection/re-radiation of radio waves, instruments, etc., can solve problems such as large amount of calculation, poor accuracy, rough logic rules, etc., and achieve strong adaptability. Effect
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specific Embodiment approach 1
[0025] Specific implementation mode one: combine figure 1 Describe this embodiment, the specific process of a kind of radar target track initiation method based on random forest in this embodiment is:
[0026] Step 1: Feature extraction is performed on the trace combinations of historical radar observation data, and the motion features (speed, acceleration, etc.) between the trace combinations and the non-motion features (signal-to-noise ratio, span, etc.) Sample set D; perform bootstrap sampling on the training sample set D to form n training sample sampling sets;
[0027] Bootstrap is a self-service sampling method; n is the number of training sample sampling sets, and the value is a positive integer;
[0028] Step 2: The t-th training sample sampling set trains the t-th decision tree, and the training sample sampling set corresponds to the decision tree one by one (training sample sampling set 1 trains decision tree 1, training sample sampling set 2 trains decision tree 2...
specific Embodiment approach 2
[0030] Specific embodiment two: the difference between this embodiment and specific embodiment one is: in the described step one, carry out feature extraction to the dot track combination of radar history observation data, extract the motion characteristic (velocity, acceleration etc.) between the dot track combination The non-motion features (signal-to-noise ratio, span, etc.) combined with dot traces form a training sample set D; the training sample set D is carried out bootstrap self-sampling to form n training sample sampling sets; the specific process is:
[0031] The point-track combination of L radar historical observation data is set as a training sample, which contains not only the real track of real target interconnection, but also the false track of false target and false target interconnection or false target and real target interconnection;
[0032] First, feature extraction is performed on the dot trace combination of the radar historical observation data, and the...
specific Embodiment approach 3
[0044] Specific embodiment three: what this embodiment is different from specific embodiment one or two is: in described step 2, the tth training sample sampling set trains the tth decision tree, and the training sample sampling set is in one-to-one correspondence with the decision tree (training Sample sampling set 1 training decision tree 1, training sample sampling set 2 training decision tree 2, ... training sample sampling set N training decision tree N), each decision tree after training is used as a base classifier to form a random forest combination classifier together, 1 ≤t≤n, n is the number of training sample sampling sets; the specific process is:
[0045] The tth training sample sampling set trains the tth decision tree, the specific process is:
[0046] Let D t ={x t～p ,y t～p} is the tth training sample sampling set, x t～p Represents the eigenvector of the pth sample of the tth sampling set, y t～p Indicates the label of the p-th sample in the t-th sampling s...
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