{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T17:13:26Z","timestamp":1773335606852,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819985548","type":"print"},{"value":"9789819985555","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8555-5_32","type":"book-chapter","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:02:36Z","timestamp":1703660556000},"page":"405-417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A BiGRU Based Adaptive Gain Estimation for Radar Multi-target Tracking"],"prefix":"10.1007","author":[{"given":"Long","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbing","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiubo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Wang D., Lian B., Liu Y., Gao B.: A cooperative UAV swarm localization algorithm based on probabilistic data association for visual measurement. IEEE Sens. J. (2022)","DOI":"10.1109\/JSEN.2022.3202356"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Wu L., Wang F., Xu Y., Jiang Y.and Wang J.: A parallel implementation of hypothesis-oriented multiple hypothesis tracking. In: 2020 IEEE 23rd International Conference on Information Fusion (FUSION), pp. 1\u20138 (2020)","DOI":"10.23919\/FUSION45008.2020.9190459"},{"key":"32_CR3","unstructured":"Mahler R.: Advances in Statistical Multisource-Multitarget Information Fusion. Artech House, MA (2014)"},{"issue":"4","key":"32_CR4","doi-asserted-by":"publisher","first-page":"2438","DOI":"10.1109\/TAES.2021.3059093","volume":"57","author":"L Gao","year":"2021","unstructured":"Gao, L., Battistelli, G., Chisci, L., Farina, A.: Fusion-based multidetection multitarget tracking with random finite sets. IEEE Trans. Aero. Elec. Syst. 57(4), 2438\u20132458 (2021)","journal-title":"IEEE Trans. Aero. Elec. Syst."},{"issue":"1","key":"32_CR5","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TII.2021.3073032","volume":"18","author":"K Shi","year":"2022","unstructured":"Shi, K., Shi, Z., Yang, C., He, S., Chen, J., Chen, A.: Road-map aided gm-phd filter for multivehicle tracking with automotive radar. IEEE Trans. Ind. Inform. 18(1), 97\u2013108 (2022)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Park, W.J., Park, C.G.: Multi-target tracking based on gaussian mixture labeled multi-bernoulli filter with adaptive gating. In: 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), pp. 226\u2013229 (2021)","DOI":"10.1109\/ICA-SYMP.2019.8646127"},{"issue":"2","key":"32_CR7","first-page":"49","volume":"28","author":"QY Li","year":"2021","unstructured":"Li, Q.Y., He, B., Zhang, X.Y.: LSTM-based Encoder-Decoder multi-step track prediction technique. Air Weapon 28(2), 49\u201354 (2021)","journal-title":"Air Weapon"},{"issue":"17","key":"32_CR8","doi-asserted-by":"publisher","first-page":"4545","DOI":"10.1109\/TSP.2019.2931170","volume":"67","author":"E Emambakhsh","year":"2019","unstructured":"Emambakhsh, E., Bay, A., Vazquez, E.: Convolutional recurrent predictor: implicit representation for multi-target filtering and tracking. IEEE Trans. Signal Process. 67(17), 4545\u20134555 (2019)","journal-title":"IEEE Trans. Signal Process."},{"key":"32_CR9","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/LSP.2020.3000679","volume":"27","author":"S Jung","year":"2020","unstructured":"Jung, S., Schlangen, I., Charlish, A.: A mnemonic kalman filter for non-linear systems with extensive temporal dependencies. IEEE Signal Process. Lett. 27, 1005\u20131009 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Milan, A., Rezatofighi, S.H., Dick, A.: Online multi-target tracking using recurrent neural networks. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.11194"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Choi, G., Park, J., Shlezinger, N.: Split-KalmanNet: a robust model-based deep learning approach for state estimation. IEEE Trans. Vehicular Technology (2023)","DOI":"10.1109\/TVT.2023.3270353"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Coskun, H., Achilles, F., DiPietro, R., Navab, N., Tombari, F.: Long short-term memory kalman filters: recurrent neural estimators for pose regularization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5525\u20135533 (2017)","DOI":"10.1109\/ICCV.2017.589"},{"key":"32_CR13","unstructured":"Xu, Y., Ban, Y., Alameda-Pineda, X.: Deepmot: A differentiable framework for training multiple object trackers. arXiv preprint arXiv:1906.06618 (2019)"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Xie, B., Dai, S.: A comparative study of extended kalman filtering and unscented kalman filtering on lie group for stewart platform state estimation. In: 2021 6th International Conference on Control and Robotics Engineering (ICCRE), pp. 145\u2013150 (2021)","DOI":"10.1109\/ICCRE51898.2021.9435722"},{"issue":"8","key":"32_CR15","doi-asserted-by":"publisher","first-page":"3447","DOI":"10.1109\/TSP.2008.920469","volume":"56","author":"D Schuhmacher","year":"2008","unstructured":"Schuhmacher, D., Vo, B.-T., Vo, B.-N.: A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process. 56(8), 3447\u20133457 (2008)","journal-title":"IEEE Trans. Signal Process."},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Rongli, G., Yan, C.: Summary of spline Curve Interpolation. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), pp. 1418\u20131421 (2020)","DOI":"10.1109\/ICMCCE51767.2020.00311"},{"issue":"2","key":"32_CR17","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3390\/act12020070","volume":"12","author":"Q Li","year":"2023","unstructured":"Li, Q., Chen, Z., Shi, W.: A novel state estimation approach for suspension system with time-varying and unknown noise covariance. Actuators 12(2), 70\u201399 (2023)","journal-title":"Actuators"},{"key":"32_CR18","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.neunet.2023.01.023","volume":"161","author":"X Huang","year":"2023","unstructured":"Huang, X.: Interpretable local flow attention for multi-step traffic flow prediction. Neural Netw. 161, 25\u201338 (2023)","journal-title":"Neural Netw."},{"key":"32_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619","volume":"219","author":"W Du","year":"2023","unstructured":"Du, W., C\u00f4t\u00e9, D., Liu, Y.: Saits: self-attention-based imputation for time series. Expert Syst. Appl. 219, 119619 (2023)","journal-title":"Expert Syst. Appl."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8555-5_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:09:53Z","timestamp":1703660993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8555-5_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"ISBN":["9789819985548","9789819985555"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8555-5_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"28 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}