# LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

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## Abstract

**:**

## 1. Introduction

## 2. Overview of the Proposed Approach

## 3. Signal Preprocessing

#### 3.1. CWD Transformation

#### 3.2. Time-Frequency Image Preprocessing

## 4. Feature Extraction and Dual-Channel CNN Model Design

#### 4.1. Feature Extraction

#### 4.2. Dual-Channel Convolutional Neural Network Model

#### 4.2.1. One-Dimensional Convolution Channel

- Gradient calculation

- 2.
- Gradient Direction Histogram Construction

#### 4.2.2. Two-Dimensional Convolution Channel

## 5. Feature Fusion and Recognition via MLP

## 6. Experimental Results and Analysis

#### 6.1. Signal Generation

#### 6.2. Recognition Accuracy Analysis

#### 6.3. Algorithmic Comparison Experiment

#### 6.4. Robustness Experiment

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**The time-frequency image preprocessing flowing chart. In the chart, we take the 4FSK signal with SNR = 0 dB as an example to illustrate this process.

Item | Model/Version |
---|---|

CPU | Intel(R) Core(TM) i7-10875H |

GPU | NVIDIA GeForce RTX 2060 |

RAM | 16 GB |

SOFTWARE | R2016b/Python 3.7 |

**Table 2.**The simulation parameter list [24].

Radar Waveform | Simulation Parameter | Ranges |
---|---|---|

Sampling frequency ${f}_{s}$ | 1 (${f}_{s}$ = 200 MHz) | |

LFM | Initial frequency ${f}_{0}$ Bandwidth $B$ | $U(1/16,1/8)$ $U(1/16,1/8)$ |

BPSK | Barker codes ${N}_{c}$ Carrier frequency ${f}_{c}$ | $\{7,11,13\}$ $U(1/8,1/4)$ |

4FSK | Fundamental frequency ${f}_{h}$ | $U(1/80,1/2)$ |

Frank and P1 | Carrier frequency ${f}_{c}$ Samples of frequency stem M | $U(1/8,1/4)$ $[4,8]$ |

P2 | Carrier frequency ${f}_{c}$ Samples of frequency stem M | $U(1/8,1/4)$ $2\times [2,4]$ |

P3 and P4 | Carrier frequency ${f}_{c}$ | $U(1/8,1/4)$ |

T1-T4 | Number of segments k | $[4,5]$ |

Saved Model/dB | Test Data/dB | Recognition Accuracy |
---|---|---|

−6 | 0 | 95% |

−4 | 2 | 92.6% |

−2 | 2 | 93% |

2 | −2 | 96.62% |

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**MDPI and ACS Style**

Quan, D.; Tang, Z.; Wang, X.; Zhai, W.; Qu, C.
LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion. *Symmetry* **2022**, *14*, 570.
https://doi.org/10.3390/sym14030570

**AMA Style**

Quan D, Tang Z, Wang X, Zhai W, Qu C.
LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion. *Symmetry*. 2022; 14(3):570.
https://doi.org/10.3390/sym14030570

**Chicago/Turabian Style**

Quan, Daying, Zeyu Tang, Xiaofeng Wang, Wenchao Zhai, and Chongxiao Qu.
2022. "LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion" *Symmetry* 14, no. 3: 570.
https://doi.org/10.3390/sym14030570