# Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar

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## Abstract

**:**

## 1. Introduction

## 2. Preliminaries

#### 2.1. Moving Vessel Detection

#### 2.2. Feature Analysis of Moving Vessels, Clutters, and Noise

#### 2.3. Moving Vessel Tracking

- (1)
- The motion and measurement models

- (2)
- State prediction

- (3)
- Plot-to-track association

- (4)
- State estimation

## 3. Methodology

#### 3.1. Plot Quality Evaluation

#### 3.1.1. Plot Feature Extraction

- Multi-directional gradient

- Local variance

- Plot position probability

#### 3.1.2. Feature Normalization

#### 3.1.3. Plot Quality Index Calculation

#### 3.1.4. Plot Quality Level Determination

#### 3.2. Plot Quality Aided Plot-to-Track Association in Dense Clutter

#### 3.2.1. Unreasonable Plots Elimination

#### 3.2.2. Calculation of Minimum Association Cost

#### 3.2.3. Selection of the Associated Plot

## 4. Results of Experiments

- (1)
- Determination of U and V.

- (2)
- Determination of $\epsilon $.

- (3)
- Determination of ${Q}_{\mathrm{min}}$.

- (4)
- Determination of $({a}_{\mathrm{M}},{a}_{\mathrm{V}},{a}_{\mathrm{P}})$ and $({w}_{v},{w}_{r},{w}_{\theta})$.

#### 4.1. Plot Quality Evaluation and Results Analysis

#### 4.2. Analysis of Tracking Performance

- (1)
- Tracking results analysis of T1

- (2)
- Tracking results analysis of T2

#### 4.3. Analysis of Computational Complexity

#### 4.4. Analysis of Parameter Sensitivity

## 5. Discussion

- (i)
- The working environment of compact HFSWR is extremely complex and a great number of false plots may be produced, which will cause plot-to-track association errors for moving vessel tracking. The proposed plot quality evaluation method can effectively evaluate the quality of plots according to their quality indexes, which can be used to filter out some false plots and provide assistant information for resolving the plot-to-track association ambiguity.
- (ii)
- The plot quality evaluation method based on spatial correlation of echo spectrum amplitudes and plot position probability may not accurately classify moving vessel plots and false plots. However, the proposed plot quality index can indicate the possibility that a plot derives from a real moving vessel. The higher the plot quality index is, the more likely the plot comes from a real moving vessel.
- (iii)
- Experimental results show that the proposed plot quality evaluation method can reasonably calculate the quality indexes of most plots. However, it is worth noting that the extracted features of false plots may be similar to those of moving vessel plots since some clutter appears in the form of blocks, so the evaluation results of these plots may be inaccurate.
- (iv)
- The NNDA method only uses kinematic parameters to calculate the similarities between measured plots and moving vessel tracks. In dense clutter scenarios, it often causes false tracking and track fragmentation due to plot-to-track association errors. The proposed plot-to-track association method introduces the plot quality index as auxiliary information to collaboratively determine the associated plot. Experimental results show that this method can effectively increase tracking time on moving vessels and improve tracking continuity.
- (v)
- During the moving vessel tracking experiments, the authors found that the proposed plot-to-track association method has better tracking performance in dense clutter scenarios, but false tracking caused by the interference of adjacent moving vessels may occur in multi-target tracking scenarios. Because the plot quality indexes of different moving vessel plots may be similar, as shown in Figure 10, it is difficult to accurately distinguish the source of moving vessel plots by their quality indexes and kinematic parameters when multiple moving vessels are close to each other. Therefore, the optimal assignment of plot-track pairs in multi-target tracking scenarios needs to be further investigated.

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

HFSWR | high-frequency surface wave radar |

CFAR | constant false alarm rate |

NNDA | nearest neighbor data association |

SNR | signal-to-noise ratio |

RCS | radar cross section |

PDA | probabilistic data association |

JPDA | joint probabilistic data association |

R-D | range-Doppler |

MUSIC | multiple signal classification |

DBF | digital beam-forming |

CMKF | converted measurement Kalman filter |

AIS | automatic identification system |

CORMS | compact over-the-horizon radar for maritime surveillance |

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**Figure 8.**The plots of five selected moving vessels on an R-D map. (

**a**) Plots detected by the radar. (

**b**) Corresponding AIS plots.

**Figure 9.**Tracks of five selected moving vessels and the selected false plots in geographic coordinate system. (

**a**) Target 1. (

**b**) Target 2. (

**c**) Target 3. (

**d**) Target 4. (

**e**) Target 5. (

**f**) False plots. The solid red dots indicate the radar location.

**Figure 13.**Tracking results comparison of T1. Point A indicates the position where the track breaks.

**Figure 14.**Tracking results comparison of T2. Point B and point C indicate the positions where the track breaks and restarts, respectively.

**Figure 15.**Comparison of tracking results obtained by JPDA and proposed methods. (

**a**) Comparison of tracking results for T1. Point A indicates the position where the track breaks. (

**b**) Comparison of tracking results for T2. Point B and point C indicate the positions where the track breaks and restarts, respectively.

Specification | Value |
---|---|

Transmitting waveform | FMICW |

Receiving antenna number | 8 |

Working frequency (MHz) | 4.7 |

Coherent integration time (s) | 262.144 |

Data rate (frame/min) | 1 |

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

U | 0.025 |

V | 4 |

$\epsilon $ | 0.2 |

${Q}_{\mathrm{min}}$ | 0.15 |

$\left({a}_{\mathrm{M}},{a}_{\mathrm{V}},{a}_{\mathrm{P}}\right)$ | (0.2, 0.4, 0.4) |

$\left({w}_{v},{w}_{r},{w}_{\theta}\right)$ | (0.7, 0.2, 0.1) |

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**MDPI and ACS Style**

Sun, W.; Li, X.; Ji, Y.; Dai, Y.; Huang, W.
Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar. *Remote Sens.* **2023**, *15*, 138.
https://doi.org/10.3390/rs15010138

**AMA Style**

Sun W, Li X, Ji Y, Dai Y, Huang W.
Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar. *Remote Sensing*. 2023; 15(1):138.
https://doi.org/10.3390/rs15010138

**Chicago/Turabian Style**

Sun, Weifeng, Xiaotong Li, Yonggang Ji, Yongshou Dai, and Weimin Huang.
2023. "Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar" *Remote Sensing* 15, no. 1: 138.
https://doi.org/10.3390/rs15010138