An improved Interacting Multiple Model Algorithm and Its Application in Airport Moving Target Tracking

LU Qixing; TANG Xinmin; QI Ming; GUAN Xiangmin

doi:10.11999/JEIT241150

Volume 47 Issue 7

Jul. 2025

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LU Qixing, TANG Xinmin, QI Ming, GUAN Xiangmin. An improved Interacting Multiple Model Algorithm and Its Application in Airport Moving Target Tracking[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2225-2236. doi: 10.11999/JEIT241150

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LU Qixing, TANG Xinmin, QI Ming, GUAN Xiangmin. An improved Interacting Multiple Model Algorithm and Its Application in Airport Moving Target Tracking[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2225-2236. doi: 10.11999/JEIT241150

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An improved Interacting Multiple Model Algorithm and Its Application in Airport Moving Target Tracking

doi: 10.11999/JEIT241150 cstr: 32379.14.JEIT241150

1.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
Key Laboratory of Technology and Equipment of Tianjin Urban Air Transportation System, Civil Aviation University of China, Tianjin 300300, China
3.
Operation Monitoring Center, Civil Aviation Administration of China, Beijing 100010, China
4.
Key Laboratory of Civil Aviation General Aviation Operation, Civil Aviation Management Institute of China, Beijing 100102, China

Funds: The National Key Research and Development Program of China (2021YFB1600500), The National Natural Science Foundation of China (52072174), Open Fund for the Key Laboratory of Civil Aviation General Aviation Operations of China Civil Aviation Management Cadre College (CAMICKFJJ-2019-04)

Received Date: 2024-12-30
Rev Recd Date: 2025-03-24

Available Online: 2025-03-27

Publish Date: 2025-07-22

Abstract

Abstract

Objective With the rapid growth of air traffic and expanding airport infrastructure, airport surfaces have become increasingly complex and congested. Higher aircraft density on taxiways and runways, increased ground vehicles, and obstacles complicate surface operations and heighten the risk of conflicts due to limited situational awareness. Pilots and ground controllers may struggle to obtain accurate environmental data, leading to potential safety hazards. To enhance surface surveillance and reduce collision risks, this study proposes an improved Interacting Multiple Model (IMM) filtering algorithm with adaptive transition probabilities. Unlike traditional IMM algorithms that rely on a fixed Markov transition probability matrix, the proposed method dynamically adjusts state transition probabilities to better adapt to operational conditions. This approach enhances tracking accuracy and improves aircraft trajectory prediction on airport surfaces, thereby increasing the safety and stability of ground operations. Methods The proposed algorithm integrates observation data and filtering residuals, constructing a fuzzy inference system for maneuver intensity using a fuzzy inference algorithm. This system infers the mapping relationship between observation data and the explicit state set in the Hidden Markov Model (HMM), deriving the corresponding state sequence. This process accurately captures target state changes, enhancing behavior prediction. The Baum-Welch algorithm in HMM is applied to solve the state transition matrix and update the observation probability matrix in real time, optimizing the adaptive update strategy for state transition probabilities. This improves model adaptability and accuracy across different environments. The algorithm integrates the fuzzy inference system for maneuver intensity with HMM and incorporates it into the IMM algorithm, forming a Fuzzy Hidden Markov-Interacting Multiple Model (FHMM-IMM) algorithm for real-time maneuvering target estimation. This approach significantly enhances tracking accuracy, particularly in complex and dynamic environments, ensuring high precision and stability for practical applications. Results and Discussions The proposed improved IMM algorithm is validated using actual airport surface ADS-B trajectory data. The results show that the algorithm adaptively adjusts parameters under non-equidistant prediction conditions, maintaining stable tracking performance (Figure 8). The position, velocity, and acceleration tracking error curves in both two-dimensional and one-dimensional Cartesian coordinates indicate a significant reduction in overall error, enhancing tracking accuracy (Figures 9, 10, and 11). Comparison with other algorithms confirms that the proposed method achieves a more stable tracking trajectory with lower errors, demonstrating superior performance (Figures 12, 13, 14, and 15). According to (Tables 2, 3, and 4), the two-dimensional position tracking accuracy improves by 63.5%, 54.3%, 40.3%, and 22.7%. The X-direction position tracking accuracy improves by 44.9%, 51.8%, 33.8%, and 35.2%, while the Y-direction position tracking accuracy improves by 63.9%, 62.9%, 52.7%, and 43.4%. The algorithm meets the real-time operational requirements of airport surface monitoring, further validating its effectiveness. Conclusions This study highlights the importance of precise four-dimensional trajectory tracking and prediction for airport surface aircraft, particularly in complex environments. Accurate trajectory tracking enhances taxiing safety and operational efficiency, addressing the challenges posed by increasing aircraft density on runways and taxiways. To improve tracking accuracy, an improved IMM algorithm with adaptive transition probabilities, based on Kalman filtering, is proposed. The main contributions are as follows: (1) A fuzzy inference system for maneuver intensity is developed, deriving explicit and hidden state sets and corresponding state sequences to capture target dynamics more accurately. (2) The FHMM-IMM algorithm is introduced for real-time estimation of maneuvering targets, incorporating time-varying state transition probabilities to enhance multi-model tracking and prediction in dynamic environments. (3) Experimental validation using real ADS-B trajectory data demonstrates that the FHMM-IMM algorithm achieves superior trajectory fitting, significantly reducing model errors. It also improves tracking accuracy for position, velocity, and acceleration in both two-dimensional and one-dimensional scenarios, verifying the effectiveness of the proposed model. These improvements provide a more precise and real-time solution for airport surface aircraft trajectory prediction and tracking, contributing to enhanced operational safety and efficiency.
- Target tracking,
- Interacting Multiple Model (IMM),
- ADS-B,
- Transition probability matrix,
- Tracking error

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References(23)

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