Volume 42 Issue 2
Apr.  2024
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CHANG Xin, MA Guanghui, GAO Jianshu, HAO Shiyu. Improved AIMM-UKF Mobile Target Tracking Algorithm Based on Airport Map Information[J]. Journal of Transport Information and Safety, 2024, 42(2): 87-94. doi: 10.3963/j.jssn.1674-4861.2024.02.009
Citation: CHANG Xin, MA Guanghui, GAO Jianshu, HAO Shiyu. Improved AIMM-UKF Mobile Target Tracking Algorithm Based on Airport Map Information[J]. Journal of Transport Information and Safety, 2024, 42(2): 87-94. doi: 10.3963/j.jssn.1674-4861.2024.02.009

Improved AIMM-UKF Mobile Target Tracking Algorithm Based on Airport Map Information

doi: 10.3963/j.jssn.1674-4861.2024.02.009
  • Received Date: 2023-11-04
    Available Online: 2024-09-14
  • Given the unique challenges posed by high-density traffic flow and diverse moving targets on airport surfaces, ensuring accurate tracking is essential for effective operation of airport automated equipment such as unmanned vehicles within airports. To address the limitations of the existing Adaptive Interactive Multi-Model-Unscented Kalman Filter algorithm (AIMM-UKF) in tracking moving targets in airport movement areas, an enhanced tracking algorithm is proposed by incorporating high precision airport map information into AIMM-UKF to improve tracking accuracy. Using the detailed airport operating procedures file from the airport map database (AMDB), the construction CAD drawing of an airport is simplified and accurately corrected with ArcGIS software and the second-order polynomial registration method to complete the high-precision airport map correction. The data collected by airport intelligent monitoring equipment is processed in real time, with the coordinate information of moving targets being corrected using the high-precision airport map information. This correction adjusts the observation values in the moving target tracking algorithm. Additionally, by incorporating adaptive correction of the Markov transition probability matrix and applying the observation matrix for secondary correction, tracking accuracy and model matching are improved. Monte Carlo simulation experiments have demonstrate that this improved algorithm utilizes high-precision airport map information to refine the observation values of moving targets. Compared with the Adaptive Correction Markov Transition Probability Matrix Interactive Multiple Model-Unscented Kalman Filter algorithm, this improved algorithm achieves an average reduction of 62.69% in the root mean square error (RMSE) of position and 56.84% in the RMSE of speed. In comparison, this algorithm exhibits superior model matching and superior filtering performance, significantly enhancing the tracking accuracy of moving targets within airport environments.

     

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