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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

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Abstract

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis ). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity.

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Acknowledgements

This research is supported by the Thales Chair of Excellence Project, Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, UAE.

Author information

Authors and Affiliations

  1. Thales/SCAI, Sorbonne University, Abu Dhabi, UAE

    Raed Abu Zitar & Esra Alhadhrami

  2. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  3. Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  4. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  5. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  6. Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Laith Abualigah

  7. Thales Land and Air Systems, Paris, France

    Frederic Barbaresco

  8. Mohammed VI Polytechnic University-Ai movement, Rabat, Morocco

    Amal ElFallah Seghrouchni

  9. Sorbonne University, Paris, France

    Amal ElFallah Seghrouchni

  10. College of Engineering, Yuan Ze University, Taoyuan 32003, Taiwan

    Laith Abualigah

  11. School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia

    Laith Abualigah

  12. Applied science research center, Applied science private university, Amman 11931, Jordan

    Laith Abualigah

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  1. Raed Abu Zitar
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  2. Esra Alhadhrami
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Zitar, R.A., Alhadhrami, E., Abualigah, L. et al. Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization. Neural Comput & Applic 36, 10501–10525 (2024). https://doi.org/10.1007/s00521-024-09602-4

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