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Sea surface floating small target detection method based on combination of multiple features and ensemble learning

A small target detection and integrated learning technology, applied in the field of signal processing, can solve problems such as poor performance of classifiers and affecting classification performance

Active Publication Date: 2020-09-25
XIDIAN UNIV
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Problems solved by technology

If the performance of the classifier is poor, it will directly affect the final classification performance

Method used

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  • Sea surface floating small target detection method based on combination of multiple features and ensemble learning
  • Sea surface floating small target detection method based on combination of multiple features and ensemble learning
  • Sea surface floating small target detection method based on combination of multiple features and ensemble learning

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Embodiment Construction

[0080] Such as figure 1 Shown, the following steps of the present invention:

[0081] A: Obtain radar echo data

[0082] Use the radar transmitter to send signals to the sea surface, and use the radar receiver to receive the echo data reflected from the sea surface to obtain the radar echo data; the echo data is divided into pure clutter data and target echo data, from the target echo data Select some distance units from wave data as training units, and the time series of training units is: z(n), n=1, 2, ..., N; the distance units of pure clutter data are used as reference units, and the time series of reference units for: z p (n), n=1, 2, ..., N, p=1, 2, ..., Q, Q is the number of reference units, and N is the length of the time series; the training unit time series z and the reference unit time series z p Respectively truncate short vectors whose length is M and do not overlap, namely:

[0083] z=[z 1 ,z 2 ,...,z m ,...,z N / M ] t (1)

[0084] z p =[z p，1 ,z p，...

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Abstract

The invention discloses a sea surface floating small target detection method based on combination of multiple features and ensemble learning. The method mainly solves the problems that a single feature is hard to guarantee performance robustness in various environments and stability is poor when a single classifier is used for detection. The method comprises the following steps of 1, acquiring radar echo data, 2, calculating normalized smooth Wigner-Willery distribution, 3, extracting sea clutters and a plurality of features of a target to form a training matrix, constructing data used for XGBoost training, 4, determining hyper-parameters of the XGBoost model by adopting K-fold cross validation and a grid search method, and training the XGBoost model, 5, inputting the multi-feature matrixof the unit to be detected into the trained XGBoost model, calculating a detection statistic D and a detection threshold T, judging whether a target exists or not according to a comparison result of the detection statistic D and the detection threshold T, judging that the target exists if the detection statistic D is greater than or equal to the detection threshold T, otherwise, judging that the target does not exist.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a method for detecting floating small targets on the sea surface based on the combination of multi-features and integrated learning. Background technique [0002] Sea clutter is the radar echo received by the radar and reflected from the sea surface. When the sea surface search radar detects the sea surface, the sea clutter will inevitably affect the detection of small floating targets such as ice floes, boats, and navigation buoys. . The strength of sea clutter will vary with radar parameters, irradiation direction, sea conditions, etc. Due to the space-time non-stationarity of high-resolution sea clutter, traditional target detection methods face the problems of low detection probability and high false alarms, making it difficult to detect small floating targets on the sea surface under the background of sea clutter. [0003] Aiming at this problem, many scholars ha...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41
CPCG01S7/414Y02A90/10
Inventor 许述文陈康权白晓惠水鹏朗
Owner XIDIAN UNIV
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