@article {Saadaoui2021134998, title = {Information sharing based on local pso for uavs cooperative search of moved targets}, journal = {IEEE Access}, volume = {9}, year = {2021}, note = {cited By 3}, pages = {134998-135011}, abstract = {This paper proposes an optimization strategy for searching moving targets{\textquoteright} locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search time and impact of uncertainties caused by the motion of targets, as well as improving the detection efficiency of UAVs. Specifically, we report, based on the UAV{\textquoteright}s scan of a location and taking into account (i) the detection and communication coverage limitations, and (ii) either a false alarm or inaccurate detection of the target, either the existence or the absence of the target. Moreover, leveraging a cooperative and competitive particle swarm optimization (PSO) algorithm, a decentralized target search model, relying on a real-time dynamic construction of cooperative UAV local sub-swarms (LoPSO), is proposed. Each sub-swarm strives to validate quickly the target location, updated based on the Bayesian theory. In such a strategy, each UAV operates in two flight modes, namely, either in swarm mode or in Greedy mode, and takes into consideration the received data from other UAVs to improve the overall environmental information. The simulation results revealed that the LoPSO outperforms other well-known searching methods of target methods for target search in unknown environments in terms of both performance and computational complexity. {\textcopyright} 2013 IEEE.}, keywords = {Aircraft detection, Antennas, Cooperative search, Decision making, Decisions makings, Information sharing, Location, Moved target, Optimization strategy, Particle swarm optimization (PSO), Sub-swarms, Target location, Target search, Unknown environments, Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)}, doi = {10.1109/ACCESS.2021.3116919}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117048719\&doi=10.1109\%2fACCESS.2021.3116919\&partnerID=40\&md5=9c295c1ff03ee777bc468333f6bc6f4e}, author = {Saadaoui, H. and Bouanani, F.E. and Illi, E.} } @conference {Saadaoui20181, title = {Information sharing based on local PSO for UAVs cooperative search of unmoved targets}, booktitle = {Proceedings - 2018 International Conference on Advanced Communication Technologies and Networking, CommNet 2018}, year = {2018}, pages = {1-6}, doi = {10.1109/COMMNET.2018.8360276}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048338815\&doi=10.1109\%2fCOMMNET.2018.8360276\&partnerID=40\&md5=8347839b2152b9792ed6dbc5fb90a466}, author = {Saadaoui, H. and El Bouanani, F.} } @conference {Saadaoui2017168, title = {Information sharing in UAVs cooperative search based on calculating the minimum time}, booktitle = {ACM International Conference Proceeding Series}, volume = {Part F130526}, year = {2017}, note = {cited By 0}, pages = {168-173}, abstract = {This paper proposes an optimization strategy for sharing and merging information of target{\textquoteright}s existence in Unmanned Aerial Vehicles (UAVs) cooperative search. That main is to minimize the search time subject to both sensing and communication limitations. We derive limits for the required number of sensor observations considering false alarms and miss detections, to declare the existence or absence of a target. the search environment is partitioned into equal-size cells, where each cell is associated with a probability of target existence and the number of visits by the UAVs, wich constitutes a probability map (search map) and a visit map (certainty map). We present a decentralized control model for cooperative target-searching and we develop a real-time approach for direct cooperation between vehicles, which is based on calculating the minimum time required to reach a cell. Each UAV takes into account the possible actions of other UAVs to increase the overall information about the environment. The simulation results illustrate the effectiveness of the proposed strategy by comparing it with the free moving strategy and show that our proposed algorithm performs out it. {\textcopyright} 2017 ACM.}, doi = {10.1145/3128128.3128154}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030314514\&doi=10.1145\%2f3128128.3128154\&partnerID=40\&md5=54300dddb081438ace965c5a6594a1b0}, author = {Saadaoui, H. and El Bouanani, F.} }