A fast preprocessing method for radar sea clutter raw data based on deep learning
A technology of raw data and deep learning, applied in radio wave measurement systems, radio wave reflection/re-radiation, instruments, etc., can solve the problems of lack of unified standards for subjective influence, high time cost, low efficiency, etc., to overcome subjective Variability and inefficiency, effect of efficient diagnosis
Active Publication Date: 2022-05-06
中国电波传播研究所
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Problems solved by technology
On the one hand, manual marking is subject to subjective influence and lacks a unified standard. On the other hand, the efficiency is low and the time cost is high. This method is acceptable when the amount of data is small, but for the continuous mass of raw radar sea clutter data, At this time, artificial preprocessing to extract the purified sea clutter data area cannot meet the demand
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Embodiment 1
[0034] Embodiment 1, this embodiment discloses a fast preprocessing method for radar sea clutter raw data based on deep learning, which inputs the amplitude map of sea clutter raw data and manually marked purified sea clutter data region positions into Faster RCNN for deep learning Network, and considering the constraints of the sea clutter clutter-to-noise ratio and the radar blind zone, extract the difference between the amplitude characteristics of the purified sea clutter data and other interference data features, and train the original data amplitude image of the sea clutter and the area position of the purified sea clutter data. The mapping relationship realizes the rapid preprocessing of the raw data of radar sea clutter. Such as figure 1 As shown, it specifically includes the following steps:
[0035] Step 1. Decode several sets of sea clutter raw data, pulse pressure, and IQ two-channel data of each set of data under the conditions of resident mode, different radar p...
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The invention discloses a method for fast preprocessing of radar sea clutter raw data based on deep learning, which includes the following steps: step 1, several groups of sea clutter raw data under the conditions of dwell mode, different radar parameters and marine environment parameters Decoding, pulse pressure; step 2, for each range gate-pulse number amplitude map in the sea clutter amplitude map data set, perform manual labeling of the purified sea clutter data area; step 3, for the sea clutter original amplitude map data set And the corresponding label files are divided into training set, verification set and test set; step 4, construct Faster RCNN image target detection deep learning network. This application provides a rapid preprocessing method for radar sea clutter raw data based on the Faster RCNN deep learning network for the raw data of radar sea clutter in the resident mode. subjective variability and inefficiency.
Description
technical field [0001] The invention belongs to the field of radar signal processing, in particular to a fast preprocessing method for raw data of radar sea clutter based on a Faster RCNN deep learning network in the field. Background technique [0002] In the application of radar to sea target detection, the key basis for determining the detection performance is the cognition, perception and application of sea clutter characteristics. Due to the lack of measured radar sea clutter data in the early stage, the method commonly used in the study of sea clutter characteristics at home and abroad is the study of the physical mechanism of sea clutter. Establish a certain empirical mapping relationship between the characteristics to realize the cognition and prediction of sea clutter in different environments. However, due to the extremely complex sea clutter generated in the actual marine environment, it is extremely difficult to deeply understand the physical mechanism of sea cl...
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IPC IPC(8): G01S7/36G01S7/40G01S13/88
CPCG01S7/36G01S7/4004G01S13/88
Inventor 张金鹏张玉石夏晓云张浙东李清亮朱秀芹胡健赵鹏
Owner 中国电波传播研究所