# Shipborne HFSWR Target Detection in Clutter Regions Based on Multi-Frame TFI Correlation

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## Abstract

**:**

## 1. Introduction

## 2. Detection Framework

#### 2.1. RD Spectrum of Shipborne HFSWR

#### 2.2. Detection Framework

## 3. Problem Formulation

#### 3.1. Spreading of Sea Clutter

#### 3.2. Modeling of Platform Movement and Motion Due to Ocean Currents

#### 3.2.1. Forward Movement and the Motion from Ocean Currents

#### 3.2.2. Heave Movement

#### 3.2.3. Sway and Surge Movements

#### 3.2.4. Roll Movement

#### 3.2.5. Pitch Movement

#### 3.2.6. Yaw Movement

#### 3.3. Simulation and Verification of Sea Clutter Spreading

## 4. Time-Frequency Analysis

#### 4.1. Time-Frequency Method

_{e}(t, f). Then the SET of the signal can be expressed as follows:

#### 4.2. Feature Extraction and Classification

## 5. Target Detection

#### 5.1. Multi-Frame Correlation and Target Detection

#### 5.2. Field Experiment

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**RD spectra of HFSWR. (

**a**) RD spectra of shore−based HFSWR. (

**b**) RD spectra of shipborne HFSWR.

**Figure 5.**Simulation and measured results. (

**a**) Frequency-domain result of simulated sea clutter. (

**b**) Simulation of RD spectrum. (

**c**) Measured RD spectrum results.

Parameter Name | Numerical Values | Unit |
---|---|---|

Roll angle | 0.2410 | rad |

Pitch angle | 1.1807 | rad |

Declination angle | 49.2000 | rad |

Sway speed | 0.2000 | m/s |

Surge speed | 0.2000 | m/s |

Roll speed | 0.1000 | m/s |

Pitch speed | 0.0900 | m/s |

Yaw Speed | 0.1700 | m/s |

Forward movement speed | 0.2000 | m/s |

Current speed | 0.3448 | m/s |

Pattern | Width (Hz) |
---|---|

Theoretical Calculations | 0.050 |

Simulation Result | 0.050 |

Measured Result | 0.048 |

Parameters | Values |
---|---|

Transmit signal | FMICW |

Operating frequency (MHz) | 4.7 |

Coherent integration time (s) | 300 |

Number of antennas | 5 |

Antenna spacing | Non-uniform array |

Type | Patch Size/Stride | Output Size | Depth | #1 × 1 | #3 × 3 Reduce | #3 × 3 | #5 × 5 Reduce | #5 × 5 | Pool Proj | Params | Ops |
---|---|---|---|---|---|---|---|---|---|---|---|

convolution | 7 × 7/2 | 112 × 112 × 64 | 1 | 2.7 K | 34 M | ||||||

max pool | 3 × 3/2 | 56 × 56 × 64 | 0 | ||||||||

convolution | 3 × 3/1 | 56 × 56 × 192 | 2 | 64 | 192 | 112 K | 360 M | ||||

max pool | 3 × 3/2 | 28 × 28 × 192 | 0 | ||||||||

Inception(3a) | 28 × 28 × 256 | 2 | 64 | 96 | 128 | 16 | 32 | 32 | 159 K | 128 M | |

Inception(3b) | 28 × 28 × 480 | 2 | 128 | 128 | 192 | 32 | 96 | 64 | 380 K | 304 M | |

Max pool | 3 × 3/2 | 14 × 14 × 480 | 0 | ||||||||

Inception(4a) | 14 × 14 × 512 | 2 | 192 | 96 | 208 | 16 | 48 | 64 | 364 K | 73 M | |

Inception(4b) | 14 × 14 × 512 | 2 | 160 | 112 | 224 | 24 | 64 | 64 | 437 K | 88 M | |

Inception(4c) | 14 × 14 × 512 | 2 | 128 | 128 | 256 | 24 | 64 | 64 | 463 K | 100 M | |

Inception(4d) | 14 × 14 × 528 | 2 | 112 | 144 | 288 | 32 | 64 | 64 | 580 K | 119 M | |

Inception(4e) | 14 × 14 × 832 | 2 | 256 | 160 | 320 | 32 | 128 | 128 | 840 K | 170 M | |

Max pool | 3 × 3/2 | 7 × 7 × 832 | 0 | ||||||||

Inception(5a) | 7 × 7 × 832 | 2 | 256 | 160 | 320 | 32 | 128 | 128 | 1072 K | 54 M | |

Inception(5b) | 7 × 7 × 1024 | 2 | 384 | 192 | 384 | 48 | 128 | 128 | 1388 K | 71 M | |

Avg pool | 7 × 7/1 | 1 × 1 × 1024 | 0 | ||||||||

Dropout(30%) | 1 × 1 × 1024 | 0 | |||||||||

Linear | 1 × 1 × 1000 | 1 | 1000 K | 1 M | |||||||

Softmax | 1 × 1 × 1000 | 0 |

Network Structure | GoogLeNet | AlexNet | VGG-16 |
---|---|---|---|

Total number of tests | 26 | 26 | 26 |

Detected correctly | 22 | 20 | 21 |

Time | 1.269 | 0.6 | 2.8 |

Accuracy | 84.6% | 76.9% | 80.77% |

Method | Our Method | CNN | Improved CFAR |
---|---|---|---|

Target total number of tests | 20 | 20 | 20 |

Detected correctly | 18 | 13 | 16 |

${P}_{\mathrm{f}}$ | 10% | 53.57% | 54.55% |

${M}_{\mathrm{r}}$ | 10% | 35% | 25% |

${P}_{\mathrm{d}}$ | 90% | 65% | 75% |

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**MDPI and ACS Style**

Wang, T.; Zhang, L.; Li, G.
Shipborne HFSWR Target Detection in Clutter Regions Based on Multi-Frame TFI Correlation. *Remote Sens.* **2022**, *14*, 4192.
https://doi.org/10.3390/rs14174192

**AMA Style**

Wang T, Zhang L, Li G.
Shipborne HFSWR Target Detection in Clutter Regions Based on Multi-Frame TFI Correlation. *Remote Sensing*. 2022; 14(17):4192.
https://doi.org/10.3390/rs14174192

**Chicago/Turabian Style**

Wang, Tao, Ling Zhang, and Gangsheng Li.
2022. "Shipborne HFSWR Target Detection in Clutter Regions Based on Multi-Frame TFI Correlation" *Remote Sensing* 14, no. 17: 4192.
https://doi.org/10.3390/rs14174192