Method for initiating radar target track based on random forest

A technology of random forest and track initiation, applied in computer parts, radio wave reflection/re-radiation, utilization of re-radiation, etc., can solve the problem of large amount of calculation, poor adaptability to strong clutter environment, and manual setting of thresholds. and other problems to achieve the effect of strong adaptability

Active Publication Date: 2018-02-13
HARBIN INST OF TECH
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  • Summary
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the existing intuitive method, logic method with rough rules, poor precision, manual setting of threshold, poor adaptability to strong clutter environment; Batch measurement data takes a long time to start, and the problem of low start probability for non-linear moving targets, and proposes a radar target track start method based on random forest

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  • Method for initiating radar target track based on random forest
  • Method for initiating radar target track based on random forest
  • Method for initiating radar target track based on random forest

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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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Abstract

The invention discloses a method for initiating a radar target track based on random forest, and relates to a method for initiating a radar target track. The method aims at solving the problems of rough rule, poor accuracy, artificial setting of threshold value, and poor suitability to strong clutter environment in the existing visual method and logic method; larger calculation amount, multiple batches of data measuring, longer initiating time, and low target initiating probability on non-linear movement in a correction Hough conversion method and the like. The method specifically comprises the following steps of 1, extracting features from a point trace combination of historical observation data of radar to form a sample set D; sampling D, and forming n training sample collection sets; 2,training a t-th decision tree by the t-th training sample collection set, and forming a random forest combination classifier; 3, in the testing phase, performing data pre-selecting and feature extraction on the point traces of a radar observation area so as to obtain the track initiating result via the classifier. The method is suitable for the field of initiating of radar target tracks.

Description

technical field [0001] The invention relates to a radar target track initiation method. Background technique [0002] Radar target track initiation refers to the track establishment process before the radar system tracks the target before entering stable tracking (track maintenance). computational burden. In general, when the track is started in the actual measurement environment, false traces (clutter) often affect the interconnection between target traces, and it is easy to generate clutter-to-clutter interconnection or clutter-to-target interconnection The initial result of the trajectory of , that is, the false alarm phenomenon. This track header will have a huge impact on subsequent association and tracking. Therefore, track initiation in complex environments is often a thorny problem. [0003] Traditional track initiation methods are mainly divided into two categories. One is the sequential processing method represented by intuitive method and logical method. The...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41G01S13/72G06K9/62
CPCG01S7/415G01S13/72G06F18/24323
Inventor 李宏博刘硕张云位寅生白杨
Owner HARBIN INST OF TECH
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