Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems
Abstract
:1. Introduction
- By processing the systematic errors interval of track coordinates and defining the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, the correlation degree between the tracks is established, and an asynchronous anti-bias TTTA algorithm based on the nearest neighbor interval average distance is proposed. This algorithm does not require time domain alignment and error registration, effectively avoiding the introduction of new estimation errors and achieving TTTA under large systematic errors.
- The average correct association rate of the tracks of the algorithm under different cycle ratios, different delay startup times, and different noise distribution forms is analyzed, demonstrating its anti-interference and effectiveness.
- By analyzing the TTTA effect of the algorithm under different systematic errors and comparing the algorithms, the good anti-bias performance of the algorithm was verified.
- The average correct association rate, the number of false associations, and the maximum false association rate of various algorithms are compared under the simulation conditions of different target numbers and processing cycles, which proves that the proposed algorithm has strong robustness and superiority.
2. Materials and Methods
2.1. Mathematical Formula Definition
2.2. Mathematical Model Establishment
2.3. Asynchronous Anti-Bias TTTA Algorithm Based on the Nearest Neighbor Interval Average Distance
2.3.1. Data Reception
2.3.2. Systematic Errors Intervalization
2.3.3. Coordinate Transformation
2.3.4. Reference Datasets Selection
2.3.5. Derivation of Track Correlation Degree Matrix
2.3.6. Asynchronous TTTA Judgment
3. Experiments and Performance Analysis
3.1. Simulation Environment and Evaluation Index
3.2. Algorithm Performance Analysis
3.2.1. Algorithm Effectiveness Analysis
3.2.2. Systematic Errors Analysis
3.2.3. Comparison of Algorithm Performance
3.2.4. Algorithm Complexity Analysis
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter Settings | Sampling Cycle | Geographical Coordinates | Oblique Distance Random Error | Azimuth Angle Random Error | Pitch Angle Random Error |
---|---|---|---|---|---|
v | 6 s | (0°, 0°, 0 m) | 50 m | 0.001 rad | 0.001 rad |
w | 4 s | (0°, 0.2°, 0 m) | 50 m | 0.001 rad | 0.001 rad |
The Processing Cycle of the FC | The Number of Targets | Maximum Oblique Distance Random Error | Maximum Azimuth Angle Random Error | Maximum Pitch Angle Random Error |
---|---|---|---|---|
50 s | 20 | 1000 m | 0.01 rad | 0.02 rad |
Sampling Period Ratio | k = 1 | k = 1.5 | k = 2 | k = 2.5 | k = 3 |
---|---|---|---|---|---|
time consumed | 0.07023 | 0.05654 | 0.05030 | 0.04888 | 0.04235 |
Startup Time Difference (s) | The Evaluation Indexes | Sampling Cycle Ratio | ||||
---|---|---|---|---|---|---|
k = 1 | k = 1.5 | k = 2 | k = 2.5 | k = 3 | ||
1 | 1 | 0 | 1 | 0 | 2 | |
99.9% | 100% | 99.9% | 100% | 99.8% | ||
1.5 | 2 | 2 | 1 | 0 | 2 | |
99.8% | 99.8% | 99.9% | 100% | 99.8% | ||
2 | 0 | 0 | 0 | 3 | 3 | |
100% | 100% | 100% | 99.7% | 99.7% | ||
2.5 | 3 | 1 | 2 | 1 | 3 | |
99.7% | 99.9% | 99.8% | 99.9% | 99.7% |
Different Noise Distribution Forms | Gaussian Distribution | Rayleigh Distribution | Exponential Distribution | Uniform Distribution |
---|---|---|---|---|
99.8% | 99.7% | 99.8% | 100% |
The Processing Cycle of the FC | The Number of Targets | Maximum Oblique Distance Random Error | Maximum Azimuth Angle Random Error | Maximum Pitch Angle Random Error |
---|---|---|---|---|
30 s | 20 | 500 m~2000 m | 0.005 rad~0.02 rad | 0.005 rad~0.02 rad |
The Processing Cycle of the FC | The Number of Targets | Maximum Oblique Distance Random Error | Maximum Azimuth Angle Random Error | Maximum Pitch Angle Random Error |
---|---|---|---|---|
30 s | 20 | 100~1500 m | 0.005~0.015 rad | 0.005~0.015 rad |
Maximum Systematic Errors | NN-IAD | SD-IRS | ||||||
---|---|---|---|---|---|---|---|---|
100 | 0.01 | 0.01 | 100.0% | 0 | 0 | 90.6% | 91 | 30% |
500 | 0.01 | 0.01 | 99.9% | 1 | 10% | 75.4% | 100 | 45% |
1000 | 0.01 | 0.01 | 99.8% | 2 | 10% | 57.8% | 100 | 50% |
1500 | 0.01 | 0.01 | 99.9% | 1 | 10% | 53.4% | 100 | 55% |
1000 | 0.005 | 0.01 | 100.0% | 0 | 0 | 53.6% | 100 | 55% |
1000 | 0.015 | 0.01 | 99.9% | 1 | 10% | 63.6% | 100 | 50% |
1500 | 0.01 | 0.005 | 99.9% | 1 | 10% | 55.6% | 100 | 50% |
1500 | 0.01 | 0.015 | 100.0% | 0 | 0 | 55.5% | 100 | 55% |
500 | 0.005 | 0.005 | 99.9% | 1 | 10% | 74.1% | 100 | 45% |
500 | 0.015 | 0.015 | 99.6% | 4 | 10% | 75.9% | 100 | 45% |
1000 | 0.005 | 0.005 | 100.0% | 0 | 0 | 63.7% | 100 | 55% |
1000 | 0.015 | 0.015 | 100.0% | 0 | 0 | 56.6% | 100 | 55% |
1500 | 0.005 | 0.005 | 99.9% | 1 | 10% | 53.5% | 100 | 55% |
1500 | 0.015 | 0.015 | 99.8% | 2 | 10% | 59.4% | 100 | 55% |
The Processing Cycle of the FC | The Number of Targets | Maximum Oblique Distance Random Error | Maximum Azimuth Angle Random Error | Maximum Pitch Angle Random Error |
---|---|---|---|---|
20:2:50 s | 10:1:50 | 1000 m | 0.01 rad | 0.02 rad |
The Processing Cycle of the FC | The Number of Targets | Maximum Oblique Distance Random Error | Maximum Azimuth Angle Random Error | Maximum Pitch Angle Random Error |
---|---|---|---|---|
30 s | 10:2:24 | 1000 m | 0.01 rad | 0.02 rad |
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Chen, S.; Ma, J.; Zhang, H.; Wang, Y. Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems. Electronics 2023, 12, 2413. https://doi.org/10.3390/electronics12112413
Chen S, Ma J, Zhang H, Wang Y. Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems. Electronics. 2023; 12(11):2413. https://doi.org/10.3390/electronics12112413
Chicago/Turabian StyleChen, Shuangyou, Juntao Ma, Hongwei Zhang, and Yinlong Wang. 2023. "Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems" Electronics 12, no. 11: 2413. https://doi.org/10.3390/electronics12112413
APA StyleChen, S., Ma, J., Zhang, H., & Wang, Y. (2023). Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems. Electronics, 12(11), 2413. https://doi.org/10.3390/electronics12112413