A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing
Abstract
:1. Introduction
2. Hierarchical Processing Model
- (1)
- Parallel reconnaissance. Parallel processing is used in a pre-stage-post-stage fashion, with pre-stage focusing on fast sorting and post-stage focusing on precise sorting, platform model identification, and comprehensive identification of “platform model + working status.” It not only meets the timeliness requirements of reconnaissance warning, it also improves the classification accuracy and identification level of advanced systems and unknown radar sources.
- (2)
- Scalable. The post-stage sorting and identification are accomplished through a scalable and open architecture. With the advancement of signal processing technology and artificial intelligence algorithms, the background reconnaissance module can be equipped with new signal characteristics and sorting and recognition algorithms.
- (3)
- Online update capability. For the unknown emitter parameters that recur after the subsequent classification and identification, the data are written into the threat database following recognition by the deep learning algorithm, allowing the threat database to be updated and upgraded online. Additionally, the background sorting and recognition algorithm can also be upgraded after combat for increased efficiency.
- (4)
- Real-time utilization of reconnaissance data. Compared to the existing alarm system’s processing method of “throwaway” data immediately after processing, the alarm architecture with pre-and post-level memory can enable data reuse and full mining of the information. Signals that cannot be processed by the preceding stage may be forwarded to the subsequent stage for processing and analysis. Additionally, it does not require ground personnel to conduct any post-analysis, which increases the warning device’s combat capability during a war. Post-processing can alleviate the processing load on an airborne electronic reconnaissance system operating in dense pulse flow conditions and improve the system’s adaptability in those conditions.
3. Radar Emitter Recognition Based on IFS
3.1. IFS Multi-Attribute Decision-Making Theory
3.2. Algorithm Processing Flow
Algorithm 1: IFS Radar Working Pattern Recognition Algorithm |
Input: Receiving radar signal Output: Emitter identification information step:
|
4. Radar Working Status Recognition based on Improved Tri-Training
4.1. The Difficulty of Classifying Unlabeled Data Using Cross Entropy
4.2. The Use of MHTW to Reduce Data Imbalance
4.3. Improved Tri-Training Algorithm Flow
Algorithm 2: Improved Tri-Training Algorithm |
Input: Marked radar signal set , unmarked radar signal set , and scaling parameter Output: Stable classifier steps:
|
5. Simulation Experiment and Analysis
5.1. Simulation Data Generation
5.2. Radar Emitter Identification Based on IFS Decision
5.3. Recognition based on the Improved Tri-Training Algorithm
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Radar Working Mode | |||
---|---|---|---|
Radar 5 | [3000, 3600] | [500, 700] | [10, 54] |
Radar 1 | [5200, 5500] | [300, 350] | [5, 10] |
Radar 3 | [9800, 11,000] | [25, 40] | [1, 7] |
Radar 4 | [9600, 9900] | [400, 1500] | [0.5, 2] |
Radar 2 | [9500, 9900] | [30, 60] | [0.5, 2] |
CPU | Clock Speed | RAM | MATLAB Version |
---|---|---|---|
Core(TM) i5-10210U CPU | 1.60 GHz | 16.0 GB | 2019 |
Radar Number | Radar 1 | Radar 2 | Radar 3 | Radar 4 | Radar 5 |
---|---|---|---|---|---|
Recognition results | 99.91% | 51.45% | 84.87% | 53.23% | 99.51% |
Algorithm | ACC/% | mAR/% | mAP/% |
---|---|---|---|
Tri-Training | 88.91 | 71.45 | 87.87 |
BP | 79.89 | 51.10 | 58.88 |
SVM | 84.86 | 66.17 | 63.14 |
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Wang, J.; Wang, X.; Tian, Y.; Chen, Z.; Chen, Y. A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing. Electronics 2022, 11, 1078. https://doi.org/10.3390/electronics11071078
Wang J, Wang X, Tian Y, Chen Z, Chen Y. A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing. Electronics. 2022; 11(7):1078. https://doi.org/10.3390/electronics11071078
Chicago/Turabian StyleWang, Jundi, Xing Wang, Yuanrong Tian, Zhenkun Chen, and You Chen. 2022. "A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing" Electronics 11, no. 7: 1078. https://doi.org/10.3390/electronics11071078
APA StyleWang, J., Wang, X., Tian, Y., Chen, Z., & Chen, Y. (2022). A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing. Electronics, 11(7), 1078. https://doi.org/10.3390/electronics11071078