Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization
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 t...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1533 |
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| _version_ | 1857415065132597249 |
|---|---|
| author | Abu Zitar, Raed |
| author2 | Alhadhrami, Esra Ebrahim Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah |
| author2_role | author author author author |
| author_facet | Abu Zitar, Raed Alhadhrami, Esra Ebrahim Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah |
| author_role | author |
| dc.creator.none.fl_str_mv | Abu Zitar, Raed Alhadhrami, Esra Ebrahim Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah |
| dc.date.none.fl_str_mv | 2024-03-19T05:20:41Z 2024-03-19T05:20:41Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 0941-0643 1433-3058 https://depot.sorbonne.ae/handle/20.500.12458/1533 10.1007/s00521-024-09602-4 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Neural Computing and Applications |
| dc.subject.none.fl_str_mv | Optimum sensors allocation Drones Multi-target tracking Complex environment Metaheuristic algorithms |
| dc.title.none.fl_str_mv | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | 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. |
| id | sorbonner_fe76aad683e6e60d7dc4880c3c01d9f9 |
| identifier_str_mv | 0941-0643 1433-3058 10.1007/s00521-024-09602-4 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1533 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimizationAbu Zitar, RaedAlhadhrami, Esra EbrahimAbualigah, LaithBarbaresco, FredericSeghrouchni, Amal ElFallahOptimum sensors allocationDronesMulti-target trackingComplex environmentMetaheuristic algorithmsThis 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.2024-03-19T05:20:41Z2024-03-19T05:20:41Z2024Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf0941-06431433-3058https://depot.sorbonne.ae/handle/20.500.12458/153310.1007/s00521-024-09602-4enNeural Computing and Applicationsoai:depot.sorbonne.ae:20.500.12458/15332024-03-19T18:00:45Z |
| spellingShingle | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization Abu Zitar, Raed Optimum sensors allocation Drones Multi-target tracking Complex environment Metaheuristic algorithms |
| title | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| title_full | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| title_fullStr | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| title_full_unstemmed | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| title_short | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| title_sort | Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization |
| topic | Optimum sensors allocation Drones Multi-target tracking Complex environment Metaheuristic algorithms |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1533 |