{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:26:07Z","timestamp":1766067967240,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62171086"],"award-info":[{"award-number":["62171086"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Global positioning systems often fall short in dense forest environments, leading to increasing demand for innovative localization methods. Notably, existing methods suffer from the following limitations: (1) traditional localization frameworks necessitate several fixed anchors to estimate the locations of targets, which is difficult to satisfy in complex and uncertain forestry environments; (2) the uncertain environment severely decreases the quality of signal measurements and thus the localization accuracy. To cope with these limitations, this paper proposes a new method of trajectory localization for forestry environments with the assistance of UAVs. Based on the multi-agent DRL technique, the topology of UAVs is optimized in real-time to cater for high-accuracy target localization. Then, with the aid of RSS measurements from UAVs to the target, the least squares algorithm is used to estimate the location, which is more flexible and reliable than existing localization systems. Furthermore, a shared replay memory is incorporated into the proposed multi-agent DRL system, which can effectively enhance learning performance and efficiency. Simulation results show that the proposed method can obtain a flexible and high-accuracy localization system with the aid of UAVs, which exhibits better robustness against high-dimensional heterogeneous data and is suitable for forestry environments.<\/jats:p>","DOI":"10.3390\/s24113398","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T09:33:31Z","timestamp":1716802411000},"page":"3398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Method of UAV-Assisted Trajectory Localization for Forestry Environments"],"prefix":"10.3390","volume":"24","author":[{"given":"Jian","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8440-1607","authenticated-orcid":false,"given":"Xiansheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/COMST.2019.2951036","article-title":"A survey on fusion-based indoor positioning","volume":"22","author":"Guo","year":"2019","journal-title":"IEEE Commun. Surv. Tuts."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Szrek, J., Tryba\u0142a, P., G\u00f3ralczyk, M., Michalak, A., Zi\u0119tek, B., and Zimroz, R. (2020). Accuracy evaluation of selected mobile inspection robot localization techniques in a GNSS-denied environment. Sensors, 21.","DOI":"10.3390\/s21010141"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, Y., Guo, Z., Wang, Y., Yao, D., Li, B., and Li, L. (2023). A survey of trajectory planning methods for autonomous driving\u2014Part I: Unstructured scenarios. IEEE Trans. Intell. Veh., 1\u201329.","DOI":"10.1109\/TIV.2023.3337318"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, C., Chu, D., Deng, Z., and Lu, L. (2024). Human-like decision making for autonomous driving with social skills. IEEE Trans. Intell. Transp. Syst., 1\u201316.","DOI":"10.1109\/TITS.2024.3366699"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dou, F., Lu, J., Wang, Z., Xiao, X., Bi, J., and Huang, C.-H. (2018, January 9\u201312). Top-down indoor localization with Wi-fi fingerprints using deep Q-network. Proceedings of the 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Chengdu, China.","DOI":"10.1109\/MASS.2018.00037"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1109\/JIOT.2023.3299262","article-title":"On-device indoor positioning: A federated reinforcement learning approach with heterogeneous devices","volume":"11","author":"Dou","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/JIOT.2017.2712560","article-title":"Semisupervised deep reinforcement learning in support of IoT and smart city services","volume":"5","author":"Mohammadi","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6279","DOI":"10.1109\/JIOT.2019.2957778","article-title":"Deep reinforcement learning (DRL): Another perspective for unsupervised wireless localization","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Testi, E., Favarelli, E., and Giorgetti, A. (2020, January 4\u20136). Reinforcement Learning for Connected Autonomous Vehicle Localization via UAVs. Proceedings of the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento, Italy.","DOI":"10.1109\/MetroAgriFor50201.2020.9277630"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"155234","DOI":"10.1109\/ACCESS.2021.3126775","article-title":"Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches","volume":"9","author":"Afifi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4035","DOI":"10.1109\/TSP.2011.2152400","article-title":"Linear least squares approach for accurate received signal strength based source localization","volume":"59","author":"So","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1109\/JIOT.2018.2871831","article-title":"Expectation maximization indoor localization utilizing supporting set for Internet of Things","volume":"6","author":"Guo","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/TCCN.2023.3330062","article-title":"Multi-agent interactive localization: A positive transfer learning perspective","volume":"10","author":"Si","year":"2024","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s11276-018-1843-8","article-title":"Estimating distances via received signal strength and connectivity in wireless sensor networks","volume":"26","author":"Miao","year":"2020","journal-title":"Wirel. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tarrio, P., Bernardos, A.M., Besada, J.A., and Casar, J.R. (2008, January 21\u201324). A new positioning technique for RSS-based localization based on a weighted least squares estimator. Proceedings of the 2008 IEEE International Symposium on Wireless Communication Systems, Reykjavik, Iceland.","DOI":"10.1109\/ISWCS.2008.4726133"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chitte, S.D., Dasgupta, S., and Ding, Z. (2009, January 17\u201319). Source localization from received signal strength under log-normal shadowing: Bias and variance. Proceedings of the 2009 2nd International Congress on Image and Signal Processing, Tianjin, China.","DOI":"10.1109\/CISP.2009.5301003"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1109\/TCYB.2020.2977374","article-title":"Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications","volume":"50","author":"Nguyen","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep reinforcement learning: A brief survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1109\/TSP.2003.814469","article-title":"Relative location estimation in wireless sensor networks","volume":"51","author":"Patwari","year":"2003","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/TMC.2011.111","article-title":"Robust relative location estimation in wireless sensor networks with inexact position problems","volume":"11","author":"Chiu","year":"2012","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zanca, G., Zorzi, F., Zanella, A., and Zorzi, M. (2008, January 1). Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. Proceedings of the Workshop on Real-world Wireless Sensor Networks, Glasgow, UK.","DOI":"10.1145\/1435473.1435475"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1109\/TSMC.2020.3037229","article-title":"A probabilistic model for driving-style-recognition-enabled driver steering behaviors","volume":"52","author":"Deng","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3398\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:33Z","timestamp":1760107713000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,25]]},"references-count":23,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113398"],"URL":"https:\/\/doi.org\/10.3390\/s24113398","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,5,25]]}}}