Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques
This paper presents two different methods for track-to-track fusion of drone tracks. The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn t...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1594 |
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| _version_ | 1857415062725066753 |
|---|---|
| author | Fares, Samar |
| author2 | Seghrouchni, Amal El Fallah Barbaresco, Frederic Abu Zitar, Raed |
| author2_role | author author author |
| author_facet | Fares, Samar Seghrouchni, Amal El Fallah Barbaresco, Frederic Abu Zitar, Raed |
| author_role | author |
| dc.creator.none.fl_str_mv | Fares, Samar Seghrouchni, Amal El Fallah Barbaresco, Frederic Abu Zitar, Raed |
| dc.date.none.fl_str_mv | 2024-05-15T07:19:06Z 2024-05-15T07:19:06Z 2024 |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1594 10.1109/AERO58975.2024.10521258 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | 2024 IEEE Aerospace Conference |
| dc.subject.none.fl_str_mv | Measurement Training Sensor phenomena and characterization Sensor fusion Radar tracking Linear programming Cost function |
| dc.title.none.fl_str_mv | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedings |
| description | This paper presents two different methods for track-to-track fusion of drone tracks. The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. The two methods are compared with other baseline methods using performance metrics such as SIAP and OSPA. Simulations are done for a single object (drone) and repeated for multiple objects in the presence of two radars to demonstrate the validity of the two proposed techniques. The JPDA (Joint Probability Data Association) with fixed gating and moderate clutter is used in the case of multiple objects. Stone Soup was chosen as the radar simulation environment. |
| id | sorbonner_ba596602ff416ec98d52857c95ea9a25 |
| identifier_str_mv | 10.1109/AERO58975.2024.10521258 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1594 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Optimum Track to Track Fusion Using CMA-ES and LSTM TechniquesFares, SamarSeghrouchni, Amal El FallahBarbaresco, FredericAbu Zitar, RaedMeasurementTrainingSensor phenomena and characterizationSensor fusionRadar trackingLinear programmingCost functionThis paper presents two different methods for track-to-track fusion of drone tracks. The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. The two methods are compared with other baseline methods using performance metrics such as SIAP and OSPA. Simulations are done for a single object (drone) and repeated for multiple objects in the presence of two radars to demonstrate the validity of the two proposed techniques. The JPDA (Joint Probability Data Association) with fixed gating and moderate clutter is used in the case of multiple objects. Stone Soup was chosen as the radar simulation environment.2024-05-15T07:19:06Z2024-05-15T07:19:06Z2024Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedingshttps://depot.sorbonne.ae/handle/20.500.12458/159410.1109/AERO58975.2024.10521258en2024 IEEE Aerospace Conferenceoai:depot.sorbonne.ae:20.500.12458/15942024-05-15T07:20:29Z |
| spellingShingle | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques Fares, Samar Measurement Training Sensor phenomena and characterization Sensor fusion Radar tracking Linear programming Cost function |
| title | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| title_full | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| title_fullStr | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| title_full_unstemmed | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| title_short | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| title_sort | Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques |
| topic | Measurement Training Sensor phenomena and characterization Sensor fusion Radar tracking Linear programming Cost function |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1594 |