# Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Radar-Jamming Signals

#### 2.1.1. Interrupted Sampling Repeater Jamming (ISRJ)

#### 2.1.2. Dense False Target Jamming (DFTJ)

#### 2.1.3. Smart Noise Jamming (SNJ)

#### 2.1.4. Blocking Jamming (BJ) and Spot Jamming (SJ)

#### 2.1.5. Linear Sweep Jamming (LSJ):

#### 2.2. Joint Time–Frequency Analysis

#### 2.2.1. Short-Time Fourier Transform (STFT)

#### 2.2.2. Wigner–Ville Distribution (WVD)

#### 2.2.3. Smoothed Pseudo-WVD (SPWVD)

#### 2.3. Pretrained Network Modification

#### 2.3.1. Convolutional Layers

#### 2.3.2. Dropout Layers

#### 2.3.3. Fully Connected Layers

## 3. Results

#### 3.1. Simulation of 1D Radar-Jamming Signals

#### 3.2. Jamming TFI Processing

#### 3.3. Modification of Deep Learning Networks

#### 3.4. Comparisons

#### 3.4.1. The More-Efficient SqueezeNet

#### 3.4.2. Comparison between STFT and WVD

#### 3.4.3. SPWVD Method Performed Better in the Event of Insufficient Samples

## 4. Conclusions and Discussion

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**The real-part waveforms of 1D active jamming signals. In each subgraph, the abscissa is discrete time, and the ordinate is normalized amplitude. (

**a**) Signal without jamming, (

**b**) Interrupted sampling repeater jamming, (

**c**) Dense false target jamming, (

**d**) Smart noise jamming, (

**e**) Blocking jamming, (

**f**) Spot jamming, (

**g**) Linear sweep jamming, (

**h**) DFTJ + SNJ, (

**i**) ISRJ + LSJ.

**Figure 5.**Three kinds of TFIs for nine types of radar-jamming signals. (

**a**) Pure noise, (

**b**) ISRJ, (

**c**) DFTJ, (

**d**) SNJ, (

**e**) BJ, (

**f**) SJ, (

**g**) LSJ, (

**h**) DFTJ + SNJ, (

**i**) ISRJ + LSJ.

Jamming | Parameter | Value |
---|---|---|

ISRJ | JNR | 30~60 dB |

Sampling duration | 5~10 μs | |

Pulse repetition | 1~4 | |

DFTJ | JNR | 30~60 dB |

False targets | 3~5 | |

False target delay | 1~10 μs | |

SNJ | JNR | 30~60 dB |

Sampling duration | 5~10 μs | |

Pulse repetition | 1~4 | |

BJ | JNR | 30~60 dB |

Jamming bandwidth | 50~80 MHz | |

SJ | JNR | 30~60 dB |

Jamming bandwidth | 20~40 MHz | |

LSJ | JNR | 30~60 dB |

Sweep bandwidth | 20 MHz | |

Sweep cycle | 40~80 μs |

Layer | Changed (Y/N) | Note | |
---|---|---|---|

End-5 | Drop | Y | 60% dropout |

End-4 | Conv | Y | nine 1-by-1 convolutions |

End-3 | ReLU | N | ReLU |

End-2 | Pool | N | 2-D global average pooling |

End-1 | Prob | N | softmax |

End | Output | Y | 1 × 9 |

Jamming | WVD | SPWVD |
---|---|---|

a | 99.98 ± 0.04 | 100.0 ± 0.00 |

b | 83.50 ± 0.00 | 99.86 ± 0.26 |

c | 100.0 ± 0.00 | 100.0 ± 0.00 |

d | 100.0 ± 0.00 | 96.30 ± 3.14 |

e | 100.0 ± 0.00 | 100.0 ± 0.00 |

f | 100.0 ± 0.00 | 97.60 ± 0.40 |

g | 99.16 ± 1.65 | 99.90 ± 0.14 |

h | 99.96 ± 0.05 | 98.38 ± 0.61 |

i | 96.88 ± 3.97 | 98.34 ± 2.53 |

OA (%) | 97.71 ± 0.38 | 98.92 ± 0.49 |

Training Ratio | ||||
---|---|---|---|---|

TFIs | 0.5% | 1% | 3% | 5% |

WVD | 89.07 ± 1.21 | 97.71 ± 0.38 | 99.82 ± 0.06 | 99.88 ± 0.02 |

SPWVD | 91.98 ± 0.76 | 98.92 ± 0.49 | 99.41 ± 0.04 | 99.79 ± 0.12 |

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**MDPI and ACS Style**

Hou, Y.; Ren, H.; Lv, Q.; Wu, L.; Yang, X.; Quan, Y.
Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning. *Symmetry* **2022**, *14*, 2318.
https://doi.org/10.3390/sym14112318

**AMA Style**

Hou Y, Ren H, Lv Q, Wu L, Yang X, Quan Y.
Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning. *Symmetry*. 2022; 14(11):2318.
https://doi.org/10.3390/sym14112318

**Chicago/Turabian Style**

Hou, Yanbin, Huidong Ren, Qinzhe Lv, Lili Wu, Xiaodong Yang, and Yinghui Quan.
2022. "Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning" *Symmetry* 14, no. 11: 2318.
https://doi.org/10.3390/sym14112318