# Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm

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## Abstract

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## 1. Introduction

- Firstly, the characteristics of ground clutter data measured in different UCA ground-based radar scenarios are studied, and the correlation, non-stationary, and statistical characteristics of the range-Doppler domain of clutter data are analyzed.
- Secondly, a GA clustering method based on chaos theory is proposed to overcome standard GA’s defects, such as premature convergence and weak local optimization ability, and complete the data classification and recognition according to the feature factors extracted from the measured clutter data.

## 2. Materials and Methods

#### 2.1. Uniform Circular Array Radar and Experiment Sites

#### 2.2. Data Pre-Processing

#### 2.3. Characteristic Factors

#### 2.3.1. Correlation Analysis of Ground Clutter Data

#### Radar Beam Correlation

#### Azimuth Correlation

#### Range Correlation

#### 2.3.2. Recursive Graph

#### 2.3.3. The Range-Doppler Maps

#### Feature Factor Extraction and Analysis

#### 2.4. Clustering Algorithms

#### 2.4.1. Standard Genetic Algorithm Clustering

#### 2.4.2. Chaotic Genetic Algorithm Clustering

## 3. Results

#### 3.1. Clustering of Clutter Data in Different Scene

- It has a faster convergence speed, which can save 34.60% of the time.
- It has a higher classification accuracy, and the average criterion function value is reduced by 42.82%.

#### 3.2. Clutter Data Clustering of Two-Beam Control Modes in The Same Scene

## 4. Conclusions

- Compared with SGA clustering, the clustering center obtained by chaotic SGA clustering is more consistent with the classification and division of actual characteristic factors. From the scene data, the criterion function values of SGA and chaotic SGA clustering corresponding to scene classification are 38.03 and 21.74, respectively, and the time consumed is 55.57 and 36.34 s, respectively. From the beam control mode of data classification, the criterion functions are 93.99 and 74.45, respectively, and the convergence speeds are 17.47 and 10.01 s, respectively.
- Chaotic SGA clustering has high local search ability and global searchability, realizing the effective classification of data samples.
- The effective classification and analysis of ground clutter data can improve UCA radar adaptability to clutter environments to enhance target detection performance.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Correlation characteristic map of UCA radar ground clutter data. (

**a**) Range time diagram of full-channel pulse splicing data; (

**b**) Beam correlation diagram of eight channels of radar; (

**c**) The variation diagram of inter pulse correlation coefficient corresponding to (

**a**); (

**d**) Intra-frame correlation function diagram of C1 beam for channel 1; (

**e**) Azimuth correlation matrix diagram of C1 beam; (

**f**) Azimuth correlation matrix diagram of C2 beam; (

**g**) Range autocorrelation of eight channels of the C1 beam; (

**h**) Range autocorrelation of eight channels of the C2 beam.

**Figure A2.**Recursive analysis results of range sampling signal of UCA radar ground clutter. The data comes from the UCA radar test experiment in the dry grassland scene. The scene’s clutter background is relatively clean, and the change of the ground structure in each distance unit within the irradiation range of the beam in the fifth channel is noticeable. (

**a**) One dimensional sampling signal in range direction; (

**b**) Phase space plan; (

**c**) State distance matrix; (

**d**) Recursive plot.

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**Figure 1.**Switching states of two-beam control modes and corresponding beam pointing simulation pattern.

**Figure 2.**UCA radar data collection for different scenarios: (

**a**) Microwave anechoic chamber test rack and probe; (

**b**) Microwave anechoic chamber rotating platform; (

**c**) Highway; (

**d**) Dry grassland; (

**e**) Town; (

**f**) Gravel land.

**Figure 3.**Radar data structure. (

**a**) The storage format of echo data; (

**b**) Radar data cube within a CPI.

**Figure 5.**Schematic diagram of multi-dimensional data correlation analysis of UCA radar. (

**a**) Schematic diagram of multi-channel single pulse data; (

**b**) Schematic diagram of multi-channel and multi-pulse data.

**Figure 6.**Schematic diagram of statistical parameter extraction of the range-Doppler map (Taking the dry grassland scene data as an example, 300 sample data are selected for the range unit, and 100 dimensions are chosen for the Doppler unit).

**Figure 8.**Clustering results of SGA (

**a**) and chaotic SGA (

**b**) in different scenarios. The data under five different experimental scenarios are selected, the population size is set to 20, the maximum number of iterations is set to 1000, and the termination condition of the minimum criterion function value is 20. In the figure on the left, the star-shaped points are the data sets, and the hollow circles are the cluster centers of the corresponding data. Note. The variables represented by the axis in the figure are dimensionless.

**Figure 9.**Clustering results of SGA (

**a**) and chaotic SGA (

**b**) in two-beam control modes. The population size is set to 15, and the maximum number of iterations is set to 500. The termination condition of the minimum criterion function value is 75. Note. The variables represented by the axis in the figure are dimensionless.

Feature Factors | Minimum | Maximum | Samples | Categories | Cluster Experiment |
---|---|---|---|---|---|

RDM-mean | 119.3997 | 139.1942 | 320 | 5 | Scene classification |

RDM-variance | 55.9213 | 166.1000 | |||

Radar beam correlation | 0.0973 | 0.6299 | |||

Azimuth correlation | 0.3490 | 0.5171 | 640 | 5 | Beam steering mode classification |

Range correlation | 0.1528 | 0.4497 | |||

Recursive rate of recursive graph | 0.1693 | 0.5601 |

Algorithm | Population Size | Number of Runs | Average Evolutionary Generation | Average of Convergence Rate (s) | Mean Value of Criterion Function | Classification Accuracy |
---|---|---|---|---|---|---|

SGA Clustering | 10 | 5 | 1000 | 29.85 | 49.52 | 20% |

15 | 5 | 1000 | 43.18 | 46.27 | 40% | |

20 | 5 | 959 | 54.52 | 35.23 | 40% | |

25 | 5 | 1000 | 69.99 | 30.53 | 60% | |

30 | 5 | 988 | 80.31 | 28.59 | 60% | |

Chaotic SGA Clustering | 10 | 5 | 933 | 28.14 | 23.63 | 40% |

15 | 5 | 827 | 35.00 | 23.11 | 60% | |

20 | 5 | 643 | 35.72 | 20.65 | 80% | |

25 | 5 | 586 | 39.53 | 20.30 | 100% | |

30 | 5 | 530 | 43.31 | 21.03 | 100% |

**Table 3.**Performance evaluation of SGA clustering and chaotic SGA clustering under two beam control modes (data from highway scene experiment).

Algorithm | Population Size | Number of Runs | Average Evolutionary Generation | Average of Convergence Rate (s) | Mean Value of Criterion Function |
---|---|---|---|---|---|

SGA Clustering | 5 | 10 | 500 | 7.76 | 132.24 |

10 | 10 | 500 | 12.45 | 89.94 | |

15 | 10 | 500 | 17.60 | 86.76 | |

20 | 10 | 472 | 21.95 | 81.52 | |

25 | 10 | 475 | 27.57 | 79.49 | |

Chaotic SGA Clustering | 5 | 10 | 340 | 4.91 | 77.82 |

10 | 10 | 281 | 7.29 | 73.42 | |

15 | 10 | 293 | 11.00 | 74.09 | |

20 | 10 | 275 | 13.00 | 73.69 | |

25 | 10 | 238 | 13.87 | 73.20 |

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**MDPI and ACS Style**

Yang, B.; Huang, M.; Xie, Y.; Wang, C.; Rong, Y.; Huang, H.; Duan, T.
Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm. *Sensors* **2021**, *21*, 4596.
https://doi.org/10.3390/s21134596

**AMA Style**

Yang B, Huang M, Xie Y, Wang C, Rong Y, Huang H, Duan T.
Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm. *Sensors*. 2021; 21(13):4596.
https://doi.org/10.3390/s21134596

**Chicago/Turabian Style**

Yang, Bin, Mo Huang, Yao Xie, Changyuan Wang, Yingjiao Rong, Huihui Huang, and Tao Duan.
2021. "Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm" *Sensors* 21, no. 13: 4596.
https://doi.org/10.3390/s21134596