Radar signal analysis method based on bidirectional long-short-term memory neural network
A long-short-term memory and neural network technology, which is applied in the field of radar signal analysis based on bidirectional long-short-term memory neural network, can solve the problem of not considering the timing of the signal, and achieve the effect of improving the accuracy.
Pending Publication Date: 2020-11-20
扬州船用电子仪器研究所
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It is precisely because radar signals are non-stable and non-linear timing signals, and traditional analysis methods do not take into account the timing between signals, so how to construct a convenient and can take into account the timing information between signals and Other important related information, and being able to handle this timing information well is an important issue
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[0022] Such as figure 2 As shown, a radar signal analysis method based on bidirectional long-short-term memory neural network, including data feature extraction, radar behavior chain construction and BLSTM model construction, the specific steps are:
[0023] Step 1: Preprocess the radar echo data and extract features;
[0024] For the radar echo data, preprocessing operations such as normalization and cleaning are performed first. Some of the directly obtained data will be missing items and have some invalid values. Cleaning here refers to checking the consistency of the data, invalid values and items, etc. Make the data format and content basically consistent; then, in order to preserve the timing characteristics of the data, reorganize the data processing and extract the corresponding features. In some embodiments, a direct windowing function or a convolutional neural network in deep learning can be used, and the purpose of feature extraction can be achieved by using a c...
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The invention discloses a radar signal analysis method based on a bidirectional long-short-term memory neural network. The radar signal analysis method specifically comprises the steps: performing preprocessing and feature extraction on radar echo data; constructing a radar behavior chain according to the extracted features, wherein the radar behavior chain is used for representing the moving track of the target; constructing a BLSTM model and inputting the radar behavior chain into the BLSTM model to train the BLSTM model; and processing the obtained radar echo signal to obtain the radar behavior chain, and inputting the radar behavior chain into the trained BLSTM model to obtain the position information of the target at the next moment. According to the invention, on the basis of the proposed radar behavior chain, a radar analysis model is constructed by adopting a bidirectional long-short-term memory neural network for processing time series data, so that on one hand, the time series between the data is reserved on the radar behavior chain, on the other hand, the incidence relation between the time series data can be better mined, and the accuracy of radar data analysis is improved.
Description
technical field [0001] The invention belongs to the technical field of radar signal processing, in particular to a radar signal analysis method based on a bidirectional long-short-term memory neural network. Background technique [0002] With the rapid development of modern science and technology such as artificial intelligence and the Internet, informatization and digitization have covered all aspects of our lives, so the entire space environment is full of various complicated signals - "big data". Radar signal is one of them. It is a random signal with non-linear and non-stationary characteristics. Therefore, whether it is in the military field or other radar application fields, how can we quickly and accurately find us in a complex wireless network environment? The required information, and the ability to correctly judge the information contained in it, the next step plan and the level of danger, etc. are still hot issues in current research. [0003] The analysis of rad...
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IPC IPC(8): G01S13/88G06N3/04G06N3/08
CPCG01S13/88G06N3/04G06N3/08
Inventor 张文君刘咏杨坡顾力伟韩光威赵艳秋吴照宪钟文王坚王喜鹏
Owner 扬州船用电子仪器研究所