{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:53:33Z","timestamp":1773240813257,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases.<\/jats:p>","DOI":"10.3390\/rs16061051","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T12:02:39Z","timestamp":1710504159000},"page":"1051","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance"],"prefix":"10.3390","volume":"16","author":[{"given":"Wenwen","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jiankang","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Dalong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-9055","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jianyin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"ref_1","first-page":"2269","article-title":"Research on adaptive Markov matrix IMM tracking algorithm","volume":"35","year":"2013","journal-title":"Syst. Eng. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hong, T., Liang, H., Yang, Q., Fang, L., Kadoch, M., and Cheriet, M. (2023). A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15010002"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.1109\/TSMC.2019.2922305","article-title":"Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm","volume":"51","author":"Xie","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bao, T., Zhang, Z., and Sabahi, M.F. (2019, January 9\u201311). An Improved Radar and Infrared Sensor Tracking Fusion Algorithm Based on IMM-UKF. Proceedings of the 16th IEEE International Conference on Networking, Sensing and Control (ICNSC), Banff, AL, Canada.","DOI":"10.1109\/ICNSC.2019.8743212"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.23919\/JSEE.2020.000089","article-title":"De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar","volume":"31","author":"Peng","year":"2020","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57795","DOI":"10.1109\/ACCESS.2019.2912983","article-title":"An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking","volume":"7","author":"Han","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103529","DOI":"10.1016\/j.dsp.2022.103529","article-title":"An adaptive IMM filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements","volume":"127","author":"Lu","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gao, L., Xing, J., Ma, Z., Sha, J., and Meng, X. (2012, January 10\u201311). Improved IMM Algorithm for Nonlinear Maneuvering Target Tracking. Proceedings of the International Workshop on Information and Electronics Engineering (IWIEE)\/International Conference on Information, Computing and Telecommunications (ICICT), Harbin, China.","DOI":"10.1016\/j.proeng.2012.01.630"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TAES.2013.6404088","article-title":"Second-Order Markov Chain Based Multiple-Model Algorithm for Maneuvering Target Tracking","volume":"49","author":"Lan","year":"2013","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, G., Zhang, X., Zeng, L., Dai, S., Zhang, M., and Lian, F. (2023). Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking. Remote Sens., 15.","DOI":"10.3390\/rs15235543"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"867","DOI":"10.23919\/JSEE.2022.000075","article-title":"Improved IMM algorithm based on support vector regression for UAV tracking","volume":"33","author":"Yuan","year":"2022","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_12","first-page":"123","article-title":"Target Tracking Algorithm Based on IMM\/MSPDAF Data Fusion of Multi-Sensor","volume":"38","author":"Rui","year":"2010","journal-title":"Mod. Def. Technol."},{"key":"ref_13","first-page":"2113","article-title":"Interacting Multiple Model Algorithm Based on Adaptive Transition Probability","volume":"45","author":"Xu","year":"2017","journal-title":"Acta Electron. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/TAC.2016.2558156","article-title":"A Study on Stability of the Interacting Multiple Model Algorithm","volume":"62","author":"Hwang","year":"2017","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1109\/TAES.2015.140542","article-title":"An Information Theoretic Approach to Interacting Multiple Model Estimation","volume":"51","author":"Li","year":"2015","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huai, L., Li, B., Yun, P., Song, C., and Wang, J. (2023). Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking. Remote Sens., 15.","DOI":"10.3390\/rs15184513"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7825","DOI":"10.1109\/TVT.2021.3093063","article-title":"Multi-Maneuvering Sources DOA Tracking with Improved Interactive Multi-Model Multi-Bernoulli Filter for Acoustic Vector Sensor (AVS) Array","volume":"70","author":"Dong","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/JAS.2023.123012","article-title":"A Novel Adaptive Kalman Filter Based on Credibility Measure","volume":"10","author":"Ge","year":"2023","journal-title":"IEEE CAA J. Autom. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/JSEN.2018.2873357","article-title":"Kalman Filtering Framework-Based Real Time Target Tracking in Wireless Sensor Networks Using Generalized Regression Neural Networks","volume":"19","author":"Jondhale","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1049\/iet-rsn.2019.0416","article-title":"Kalman filtering method for sparse off-grid angle estimation for bistatic multiple-input multiple-output radar","volume":"14","author":"Baidoo","year":"2020","journal-title":"Iet Radar Sonar Navig."},{"key":"ref_21","first-page":"1591","article-title":"IMM-CKF and Posterior Cram\u00e9r-Rao Lower Bound for a Highly Maneuvering Target","volume":"63","author":"RadhikaM","year":"2020","journal-title":"Solid State Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, Z., Gao, R., and He, W. (2021, January 18\u201320). A Review of The Research on Kalman Filtering in Power System Dynamic State Estimation. Proceedings of the IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, Chongqing, China.","DOI":"10.1109\/IMCEC51613.2021.9482112"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1049\/iet-spr.2019.0166","article-title":"Kalman and UFIR state estimation with coloured measurement noise using backward Euler method","volume":"14","author":"Shmaliy","year":"2020","journal-title":"Iet Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1109\/TVT.2008.928649","article-title":"Mobile Location Estimator in a Rough Wireless Environment Using Extended Kalman-Based IMM and Data Fusion","volume":"58","author":"Chen","year":"2009","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Quanan, G., and Yunjian, X. (2020, January 12\u201313). Kalman Filter Algorithm for Sports Video Moving Target Tracking. Proceedings of the 2020 International Conference on Advance in Ambient Computing and Intelligence (ICAACI), Ottawa, ON, Canada.","DOI":"10.1109\/ICAACI50733.2020.00010"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/TVT.2014.2329497","article-title":"An IMM\/EKF Approach for Enhanced Multitarget State Estimation for Application to Integrated Risk Management System","volume":"64","author":"Kim","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2993675","DOI":"10.1155\/2021\/2993675","article-title":"Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching","volume":"4","author":"Du","year":"2021","journal-title":"Complexity"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1109\/TIP.2017.2779856","article-title":"Multiple Pedestrian Tracking from Monocular Videos in an Interacting Multiple Model Framework","volume":"27","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","first-page":"53.1","article-title":"A Novel Adaptive Markov Matrix IMM Algorithm using Multi-Sensor Fusion","volume":"17","author":"Li","year":"2016","journal-title":"Int. J. Simul. Syst. Sci. Technol."},{"key":"ref_30","unstructured":"Xiao, J., Wang, H., Xu, W., Xu, D., and Cao, J. (September, January 31). Application of an Adaptive IMM Algorithm with Optimized Tracking Performance in PD Radar. Proceedings of the 16th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies (UCMMT), Guangzhou, China."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1002\/tee.23575","article-title":"Maneuvering Target Tracking with Multi-Model Based on the Adaptive Structure","volume":"17","author":"Guo","year":"2022","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sabilla, I.A., Meirisdiana, M., Sunaryono, D., and Husni, M. (2021, January 14\u201315). Best Ratio Size of Image in Steganography using Portable Document Format with Evaluation RMSE, PSNR, and SSIM. Proceedings of the 4th IEEE International Conference on Computer and Informatics Engineering (IC2IE), Depok, Indonesia.","DOI":"10.1109\/IC2IE53219.2021.9649198"},{"key":"ref_33","first-page":"1226","article-title":"Adaptive multiple-model tracking algorithm based on STC-IMM structure","volume":"28","author":"Zhou","year":"2013","journal-title":"Control Decis."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/6\/1051\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:14:28Z","timestamp":1760105668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/6\/1051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16061051"],"URL":"https:\/\/doi.org\/10.3390\/rs16061051","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,15]]}}}