Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
Abstract
:1. Introduction
- (1)
- Introducing a multidimensional adaptive factor diagonal matrix solves the problem of a single adaptive factor adjustment of strong tracking UKF under multidimensional system variables and realizes the flexible adjustment in a multidimensional state.
- (2)
- Introducing the criterion of discriminating the matching degree between the actual residual covariance and the theoretical residual covariance, which makes up for the lack of practical judgment and adjustment mechanism when the noise statistical characteristics change.
- (3)
- The dynamic adjustment strategy based on UT transform solves the problem of increasing filtering error when the target motion state changes significantly.
2. Related Work
3. Multidimensional Adaptive Factor-Based Strong Tracking UKF Algorithm
3.1. Introduction of Multidimensional Adaptive Decay Factors
3.2. MAST-UKF Algorithm Design
3.3. Simulation Experiments and Analysis
4. Real Experimental Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Time | Motion Model |
---|---|
0–40 s | (70 m/s, 0 m/s) CV motion |
41–75 s | Constant Turn (CT) motion with angular velocity of 0.1 rad/s |
76–100 s | Constant Acceleration (CA) motion with acceleration of (0.1 m/s², 0 m/s²) |
101–135 s | Constant Turn (CT) motion with angular velocity of −0.1 rad/s |
136–175 s | (70 m/s, 0 m/s) CV motion |
Tracking Algorithm | X-Axis Position (m) | Y-Axis Position (m) | X-Axis Velocity (m/s) | Y-Axis Velocity (m/s) |
---|---|---|---|---|
UKF | 2.3216 | 1.3404 | 1.2297 | 0.7064 |
ST-UKF | 2.1833 | 1.2605 | 1.1925 | 0.6924 |
AST-UKF | 2.0679 | 1.1939 | 1.1066 | 0.6396 |
MAST-UKF | 1.7485 | 1.0057 | 0.7704 | 0.4456 |
Radar System Parameters | Parameter Value | Unit |
---|---|---|
Operating Frequency Range | 60–61 | GHz |
Number of TX/RX Antenna Array | 4T/4R | - |
Signal Bandwidth | 200 | MHz |
Signal Pulse Period | 30 | μs |
Tuning Frequency Rate | 6.7 | MHz/μs |
Distance Resolution | 0.75 | m |
Speed Resolution | 0.1 | m/s |
Angle Resolution | 0.1 | ° |
Data Rate | 20 | Hz |
Tracking Algorithm | A X-Axis Position (m) | A Y-Axis Position (m) | B X-Axis Position (m) | B Y-Axis Position (m) |
---|---|---|---|---|
AST-UKF | 0.1705 | 0.7218 | 0.1039 | 0.7052 |
MAST-UKF | 0.1568 | 0.4892 | 0.0971 | 0.5193 |
Tracking Algorithm | A X-Axis Position (m) | A Y-Axis Position (m) | B X-Axis Position (m) | B Y-Axis Position (m) |
---|---|---|---|---|
AST-UKF | 0.1970 | 1.2894 | 0.1638 | 1.1379 |
MAST-UKF | 0.1836 | 0.9372 | 0.1547 | 0.8836 |
Rounds | AST-UKF | MAST-UKF |
---|---|---|
1 | 44.222 s | 48.285 s |
2 | 43.572 s | 48.398 s |
3 | 44.171 s | 48.438 s |
4 | 43.865 s | 48.438 s |
5 | 43.686 s | 47.876 s |
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Tian, F.; Wang, S.; Fu, W.; Wei, T. Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter. Appl. Sci. 2025, 15, 3276. https://doi.org/10.3390/app15063276
Tian F, Wang S, Fu W, Wei T. Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter. Applied Sciences. 2025; 15(6):3276. https://doi.org/10.3390/app15063276
Chicago/Turabian StyleTian, Feng, Siyuan Wang, Weibo Fu, and Tianyu Wei. 2025. "Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter" Applied Sciences 15, no. 6: 3276. https://doi.org/10.3390/app15063276
APA StyleTian, F., Wang, S., Fu, W., & Wei, T. (2025). Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter. Applied Sciences, 15(6), 3276. https://doi.org/10.3390/app15063276