{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:55:32Z","timestamp":1778345732695,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62301599"],"award-info":[{"award-number":["62301599"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Inverse synthetic aperture radar (ISAR) three-dimensional (3D) imaging technology enables the acquisition of clear 3D structures of targets, significantly enhancing target recognition performance. In resource-constrained environments, an effective resource scheduling algorithm is essential for achieving high-quality 3D imaging of multiple targets. However, existing algorithms often neglect the quality requirements of 3D imaging during resource allocation. A resource scheduling algorithm for multi-target 3D imaging in a radar network based on deep reinforcement learning (DRL) is proposed in this paper, achieving multi-target 3D imaging with minimal time resource consumption while ensuring the imaging quality of targets. First, based on the projection-based multi-view ISAR 3D imaging method, the impact of the radar distribution and radar number on the target imaging quality is analyzed. Subsequently, a resource scheduling model is constructed with the objective of minimizing time consumption while ensuring target imaging quality. The problem is then formulated as a Markov decision process, and the Advantage Actor\u2013Critic (A2C) deep reinforcement learning method is employed to solve the model. By reasonably designing the reward for reinforcement learning and pruning the action space based on domain knowledge, the convergence speed of the network is significantly accelerated. An optimal scheduling strategy including a radar node allocation scheme and timing pulse allocation scheme for each radar can be obtained after convergence. The simulation experiments validate the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/rs16234472","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T08:15:54Z","timestamp":1732781754000},"page":"4472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Resource Scheduling Algorithm for Multi-Target 3D Imaging in Radar Network Based on Deep Reinforcement Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0558-9202","authenticated-orcid":false,"given":"Huan","family":"Yao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Engineering University of the People\u2019s Armed Police, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8171-1718","authenticated-orcid":false,"given":"Hao","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Engineering University of the People\u2019s Armed Police, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3157-4886","authenticated-orcid":false,"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9264-7490","authenticated-orcid":false,"given":"Yijun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Engineering University of the People\u2019s Armed Police, Xi\u2019an 710086, China"},{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3828-6812","authenticated-orcid":false,"given":"Junkun","family":"Yan","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9808","DOI":"10.1109\/JSEN.2023.3263591","article-title":"Measurement Matrix Optimization Based on Target Prior Information for Radar Imaging","volume":"23","author":"Chen","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5494","DOI":"10.1109\/TAES.2022.3174826","article-title":"Robust ISAR Target Recognition Based on ADRISAR-Net","volume":"58","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, X.W., Zhang, Q., Jiang, L., Liang, J., and Chen, Y.J. (2018). Reconstruction of Three-Dimensional Images Based on Estimation of Spinning Target Parameters in Radar Network. Remote Sens., 10.","DOI":"10.3390\/rs10121997"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lupidi, A., Giusti, E., Tomei, S., Ghio, S., and Martorella, M. (2024, January 6\u201310). Target Recognition by Means of 3D ISAR Images. Proceedings of the 2024 IEEE Radar Conference (RadarConf24), Denver, CO, USA.","DOI":"10.1109\/RadarConf2458775.2024.10548952"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, H., Qin, G., and Zhang, L. (2007, January 15\u201317). Monopulse radar 3-D imaging and application in terminal guidance radar. Proceedings of the MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, Wuhan, China.","DOI":"10.1117\/12.742162"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"132415","DOI":"10.1109\/ACCESS.2020.3010225","article-title":"High Precision Cross-Range Scaling and 3D Geometry Reconstruction of ISAR Targets Based on Geometrical Analysis","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TCI.2023.3248942","article-title":"Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets","volume":"9","author":"Kumar","year":"2023","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Ding, C., Chen, L., and Zhang, F. (2018). Three-dimensional imaging method for array ISAR based on sparse Bayesian inference. Sensors, 18.","DOI":"10.3390\/s18103563"},{"key":"ref_9","first-page":"5100414","article-title":"3-D Scattering Image Sparse Reconstruction via Radar Network","volume":"60","author":"Kang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TSP.2020.2976587","article-title":"Resource Scheduling for Distributed Multi-Target Tracking in Netted Colocated MIMO Radar Systems","volume":"68","author":"Yi","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"15434","DOI":"10.1109\/JSEN.2024.3379384","article-title":"Integrated Trajectory Planning and Resource Scheduling for Multiple Target Tracking in Airborne Radar Network","volume":"24","author":"Dai","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3664","DOI":"10.1109\/TAES.2024.3367662","article-title":"Deployment Optimization for Integrated Search and Tracking Tasks in Netted Radar System Based on Pareto Theory","volume":"60","author":"Yan","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/TAES.2023.3347214","article-title":"Multidomain Resource Allocation for Asynchronous Target Tracking in Heterogeneous Multiple Radar Networks With Nonideal Detection","volume":"60","author":"Shi","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1109\/TSP.2024.3367278","article-title":"Decentralized Resource Allocation for Multi-Radar Systems Based on Quality of Service Framework","volume":"72","author":"Yuan","year":"2024","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5096","DOI":"10.1109\/TSP.2015.2449251","article-title":"An Adaptive ISAR-Imaging-Considered Task Scheduling Algorithm for Multi-Function Phased Array Radars","volume":"63","author":"Chen","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7541","DOI":"10.1109\/JSEN.2021.3049899","article-title":"A Cooperative Task Allocation Game for Multi-Target Imaging in Radar Networks","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/JSEN.2019.2954711","article-title":"Time and Aperture Resource Allocation Strategy for Multitarget ISAR Imaging in a Radar Network","volume":"20","author":"Du","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"016521","DOI":"10.1117\/1.JRS.15.016521","article-title":"Resource scheduling algorithm optimization for multitarget inverse synthetic aperture radar imaging in radar network","volume":"15","author":"Wang","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4462","DOI":"10.1109\/JSEN.2020.3029430","article-title":"Radar Network Time Scheduling for Multi-Target ISAR Task With Game Theory and Multiagent Reinforcement Learning","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_20","unstructured":"Yang, L., Liao, K., Ouyang, S., and Xie, N. (2022, January 28\u201331). Resource scheduling algorithm for multi-target imaging in netted radar based on SIMO technology. Proceedings of the 7th Asia Pacific Conference on Optics Manufacture, Shanghai, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, Q., Jiang, L., Yu, X., Zhou, C., and Wang, Z. (2020, January 14\u201316). Sparse Aperture Based Radar Observation Resource Allocation Algorithm for Space Target 3D Imaging. Proceedings of the 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China.","DOI":"10.1109\/CCET50901.2020.9213155"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13634-022-00866-3","article-title":"An adaptive task scheduling algorithm for 3-D target imaging in radar network","volume":"2022","author":"Wang","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_23","first-page":"7078","article-title":"ISAR imaging resource-scheduling algorithm in network radar based on information fusion","volume":"2019","author":"Li","year":"2019","journal-title":"J. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, T., Liao, K., Ouyang, S., and Wang, H. (2022). Resource Scheduling for Multitarget Imaging in a Distributed Netted Radar System Based on Maximum Scheduling Benefits. Sensors, 22.","DOI":"10.3390\/s22176400"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nocedal, J., and Wright, S.J. (1999). Numerical Optimization, Springer.","DOI":"10.1007\/b98874"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"04766","DOI":"10.1016\/j.jpdc.2023.104766","article-title":"Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach","volume":"183","author":"Behera","year":"2024","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"133633","DOI":"10.1109\/ACCESS.2020.3009039","article-title":"Real-time optimal scheduling of large-scale electric vehicles: A dynamic non-cooperative game approach","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2576","DOI":"10.1109\/TSMC.2023.3345928","article-title":"Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling","volume":"54","author":"Song","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23117","DOI":"10.1109\/JSEN.2022.3211606","article-title":"Domain Knowledge-Assisted Deep Reinforcement Learning Power Allocation for MIMO Radar Detection","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_30","first-page":"1008","article-title":"Actor-critic algorithms","volume":"12","author":"Konda","year":"1999","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alibabaei, K., Gaspar, P.D., Assun\u00e7\u00e3o, E., Alirezazadeh, S., Lima, T.M., Soares, V.N., and Caldeira, J.M. (2022). Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization: A case study at a site in Portugal. Computers, 11.","DOI":"10.3390\/computers11070104"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","article-title":"Reinforcement learning: A survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J. Artif. Intell. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:41:46Z","timestamp":1760114506000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"references-count":32,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234472"],"URL":"https:\/\/doi.org\/10.3390\/rs16234472","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]}}}