Publications
Multi-object tracking + Particle filter + Beyond (J. refers journal publication while C. refers conference)
O2/C4F/F4S: Novel approaches for Detection, Estimation, and Tracking (DET)
J.9) T. Li, Y. Song, H. Fan, From Target Tracking to Targeting Track: A Data-Driven Yet Analytical Approach to Joint Target Detection and Tracking, Signal Processing, 205, 2023, 108883 @Sciencedirect
J.8) C. Liu, K. Di, T. Li*, V. Elvira. A sensor selection approach to maneuvering target tracking based on trajectory function of time. EURASIP J. Adv. Signal Process. 2022, 72 (2022).
J.7) J. Zhou, T. Li, X. Wang and L. Zheng, Target Tracking with Equality/Inequality Constraints Based on Trajectory Function of Time, IEEE Signal Processing Letters, , vol. 28, pp. 1330-1334, 2021. @IEEE
J.6) T. Li, H. Chen, S. Sun and J. M. Corchado. Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting, IEEE Trans. Automation Science and Engineering, vol.16, no.3, pp.1476-1483, 2019. @ IEEE Xplore Pre-print @ arXiv:1708.02196 [stat.AP]
J.5) T. Li, Single-road-constrained positioning based on deterministic trajectory geometry, IEEE Communications Letters, , vol.23, no.1, pp. 80-83, 2019 @ IEEEXplore
J.4) T. Li, A Robust Multi-Sensor PHD Filter Based on Multi-Sensor Measurement Clustering，IEEE Communications Letters, vol.22, no.10, pp. 2064-2067, 2018 @ IEEE Xplore
J.3) T. Li, F. De la Prieta Pintado, J. M. Corchado, J. Bajo, Multi-source Homogeneous Data Clustering for Multi-target Detection from Cluttered Background with Misdetection, Applied Soft Computing 60 (2017) 436–446 @ ScienceDirect. The source code for an illustrative MODE example is available @ C4F
J.2) T.Li, J.M. Corchado,S. Sun and J. Bajo, Clustering for filtering: multi-object detection and estimation using multiple/massive sensors, Information Sciences, Vol. 388–389, May 2017, Pages 172-190. @ ScienceDirect.
J.1) T.Li, J.M. Corchado, J. Bajo, S. Sun and J. F. Paz, Effectiveness of Bayesian Filters: An Information Fusion Perspective, Information Sciences, 2016, 329: 670-689. @ ScienceDirect
c.10) Y. Xin, Y. Song and T. Li*, "A Metric for Multi-Target Continuous-Time Trajectory Evaluation," 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022, pp. 364-370
c.9) Z. Hu and T. Li*, "A Particle Bernoulli Filter Based on Gaussian Process Learning for Maneuvering Target Tracking," 2022 30th European Signal Processing Conference (EUSIPCO), 2022, pp. 777-781
c.8) J. Zhou, T. Li* and X. Wang, "State Estimation with Linear Equality Constraints Based on Trajectory Function of Time and Karush-Kuhn-Tucker Conditions," 2021 International Conference on Control, Automation and Information Sciences (ICCAIS), 2021, pp. 438-443
C.7）T. Li, X. Wang, Y. Liang, J. Yan and H. Fan, A Track-oriented Approach to Target Tracking with Random Finite Set Observations, ICCAIS 2019, Chengdu, China, Oct. 22-24, 2019 @IEEE Xplore
C.6) T. Li, J.M. Corchado and H. Chen, Distributed Flooding-then-Clustering: A Lazy Networking Approach for Distributed Multiple Target Tracking, FUSION 2018, Cambridge, UK, July 10-13 2018. @IEEE Xplore
C.5) T. Li, J.M. Corchado, H. Chen and J. Bajo, Track A Smoothly Maneuvering Target Based on Trajectory Estimation, Proceedings of 20th Int. Conf. on Information Fusion, pp. 800-807, Xi’an, China, July 10-13, 2017. @myResearchGate
C.4) T. Li, J. Prieto, J. M. Corchado, Fitting for Smoothing: A Methodology for Continuous-Time Target Track Estimation, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), At Alcalá de Henares, Spain, Oct. 4-7, 2016. @ IEEE
C.3) T. Li, J. M. Corchado, J. Bajo and S. Sun, Multi-source Data Clustering, FUSION 2015, pp. 830-837, Washington, D.C., U.S, July 6-9, 2015. @ IEEE Xplore
C.2) T. Li, J. Prieto, J. M. Corchado and J. Bajo, On the use and misuse of Bayesian filters, FUSION 2015, pp.838-845, Washington, D.C., July 6-9, 2015 @ IEEE Xplore
C.1) T. Li, J. M. Corchado, J. Bajo and G. Chen, Multi-Target Detection and Estimation with the Use of Massive Independent, Identical Sensors, Proceedings of SPIE Vol. 9469-15, Baltimore, Maryland, US, April 20-24, 2015. @ SPIE library
Distributed (multi-sensor) tracking + Multi-object tracking:
J.25) H. Yang, T.Li*, J. Yan, V. Elvira, Hierarchical Average Fusion with GM-PHD Filters Against FDI and DoS Attacks, IEEE Signal Processing Letters, DOI: 10.1109/LSP.2024.3356823
J.24) T. Li, R. Yan, K. Da, H Fan, Arithmetic Average Density Fusion Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion, IEEE TAES, vol. 60, no. 1, pp. 1023-1034, Feb. 2024 @IEEE Xplore
J.23) T. Li, Arithmetic Average Density Fusion Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion. IEEE TAES, vol. 60, no. 3, pp. 3255-3268, June 2024 @IEEE Xplore
J.22) T. Li, Y. Song, E. Song, H. Fan, Arithmetic Average Density Fusion Part I: Some Statistic and Information-theoretic Results. Information fusion, 2024, 104, 102199 @sciencedirect
J.21) T.Li*, H. Liang, B. Xiao, Q. Pan and Y. He, Finite mixture modeling in time series: A survey of Bayesian filters and fusion approaches, Information Fusion, 2023, 98: 101827 @ScienceDirect
J.20) T. Li, Z. Hu, Z. Liu and X. Wang, Multi-sensor Suboptimal Fusion Student's t Filter, IEEE T-AES, vol. 59, no. 3, pp. 3378-3387, June 2023. @IEEE Xplore
J.19) T.Li, et al, Best Fit of Mixture for Computationally Efficient Multi-sensor Poisson Multi-Bernoulli Mixture Filtering, Signal Process., Vol. 202, 2023, 108739 @ ScienceDirect
J.17) B. Yu, T. Li*, S. Ge, H. Gu, "Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors", IEEE Sensors Journal, vol. 22, no. 1, pp. 1030-1040, 2022.
J.16) F. Yang, L. Zheng, T. Li*, and L. Shi, A computationally efficient distributed Bayesian filter with random finite set observations, Signal Processing, Volume 194, May 2022, 108454.
J.15) T.Li, Z. Liu, et al, Best Fit of Mixture For Multi-Sensor Poisson Multi-Bernoulli Mixture Filtering, under revision in IEEE Trans. Signal Process., DOI: 10.36227/techrxiv.12351710, 2020. URL
J.14) T. Li, F. Hlawatsch, A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters, Information Fusion，73: 111-124, 2021. ScienceDirect
J.13) T. Li, S. Sun, M. Bolic, J. M. Corchado, A Parallel Filtering-Communication based Cardinality Consensus Approach for Real-time Distributed PHD Filtering, IEEE Sensors Journal, vol. 20, no. 22, pp. 13824-13832, 15 Nov.15, 2020 IEEE eXplore
J.12) T.Li, X. Wang, Y. Liang and Q. Pan, On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking, IEEE Trans. Signal Process. 68: 2883–2896, 2020. IEEE eXplore
J.11) T.Li*, Z. Liu and Q. Pan, Distributed Bernoulli Filtering for Target Detection and Tracking Based on Arithmetic Average Fusion, IEEE Signal Processing Letters. Vol.26, no.12, pp. 1812-1816, 2019. IEEE Xplore
J.10) T. Li, H. Fan, J.G. Herrero and J M Corchado, Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion: Application to Multi-sensor Target Tracking, Information Fusion, vol. 51, pp. 233-243, 2019. @ sciencedirect
J.9) T. Li, J.M. Corchado and S. Sun, Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion, IEEE Trans. Aeros. Electr. Syst., vol.55, no.5, 2150-2163, 2019. PrePrint is available @ IEEE Xplore
J.8) T. Li, E. Víctor, H. Fan and J. Corchado, Local-Diffusion-based Distributed SMC-PHD Filtering Using Sensors with Limited Sensing Range, IEEE Sensors Journal, 2019, vol.19, no.4, pp.1580 - 1589. IEEE Xplore
J.7) T. Li, F. Hlawatsch, P. Djuric, Cardinality-consensus-based PHD filtering for distributed multi-target tracking, IEEE Signal Process. Lett. vol.26, no.1, 2019, pp.49-53. IEEE Xplore
c.10) F. Yang, X. Tang, T. Li and L. Zheng, "Learning-based Gaussian Mixture Reduction for Distributed Bayesian Filter," 2021 International Conference on Control, Automation and Information Sciences (ICCAIS), 2021, pp. 782-787
c.9) C. Xia, H. Yang, T. Li*, Distributed Indegree-Weighted Kalman Consensus Filter Algorithm Against Packet-Dropping. 2023 International Conference on Control Automation and Information Sciences (ICCAIS 2023) , Hanoi, Vietnam, 26-30 Nov. 2023.
c.8) H. Li, G. Li, T. Li*，Information Gain-Weighted Multi-Sensor Arithmetic Average Fusion Kalman Filtering, 2023 International Conference on Control Automation and Information Sciences (ICCAIS 2023), Hanoi, Vietnam, 26-30 Nov. 2023
c.7) K. Da, T. Li, Y. Zhu, H. Fan, Q. Fu, Kullback-Leibler Averaging for Multitarget Density Fusion. In: Herrera F., Matsui K., Rodríguez-González S. (eds) Proceedings of DCAI 2019, Springer, Cham, pp 253-261.
C.6) T. Li, J.M. Corchado and S. Sun, On Generalized Covariance Intersection for Distributed PHD Filtering and a Simple but Better Alternative, Proceedings of 20th Int. Conf. on Information Fusion, pp. 808-815, Xi’an, China, July 10-13, 2017.
Extended version: Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion, IEEE Trans. Aeros. Electr. Syst., 2018, fully accepted, DOI: 10.1109/TAES.2018.2882960 IEEE XploreC.5) T. Li, J. M. Corchado, Jesús García, Jvier Bajo, MEAP: Approximate optimal estimate extraction for the SMC-PHD filter, 19th International Conference on Information Fusion (FUSION 2016), Heidelberg, Germany, 2016, pp. 2309-2316. @ IEEE Xplore
C.4) T. Li, S. Sun, J. M. Corchado and M. F. Siyau, A Particle Dyeing Approach for Track Continuity for the SMC-PHD filter, the 17th International Conference on Information Fusion(FUSION 2014), Salamanca, Spain, July 7-10, 2014. @ IEEE Xplore
C.3) T. Li, S. Sun, J. M. Corchado and M. F. Siyau, Random Finite Set-Based Bayesian Filters Using Magnitude-adaptive Target Birth Intensity, FUSION 2014, Salamanca, Spain, July 7-10, 2014. @ IEEE Xplore
C.2) T. Li, T. P. Sattar, S. Sun, Q. Han, Roughening methods to prevent sample impoverishment in the particle PHD filter, FUSION 2013, Istanbul Turkey, 9-12 July 2013.
C.1) T. Li, T. P. Sattar, Z. Zhao, A thorough study of the stability of the PHD filter, Sensor Signal Processing for Defense (SSPD 2012), Imperial College, London, 25-27 September, 2012.
J.6) T. Li, J.M. Corchado, J. Prieto. Convergence of distributed flooding and its application for distributed Bayesian filtering, IEEE Trans. Signal and Information Processing over Networks, 2017, vol.3, no.3, pp. 580-591. @ IEEE Xplore
J.5) T. Li, J.M. Corchado, S. Sun and H. Fan. Multi-EAP: Extended EAP for multi-estimate extraction for the SMC-PHD filter, Chinese Journal of Aeronautics, Vol. 30, No. 1, PP. 368-379, 2017. OPEN ACCESS @ Sciencedirect
J.4) J. M. Corchado, Li Weigang, J. Bajo, F. Wu and T. Li*, Special issue on distributed computing and artificial intelligence, Front. Inform. Technol. Electron. Eng. 17(4) 2016: 281-282. @ Springer
J.3) T. Li, S. Sun, M. Bolic and J. M. Corchado. Algorithm design for parallel implementation of the SMC-PHD filter, Signal Processing, 2016, vol.119, pp. 115-127. @ ScienceDirect
J.2) T. Li, S. Sun, Online Adapting the magnitude of target birth intensity in the PHD filter, Advances in Distributed Computing and Artificial Intelligence Journal, 2013, vol.1, no.7, pp. 31-40.
J.1) T. Li, S. Sun and T. P. Sattar, High-speed sigma-gating SMC-PHD filter, Signal Processing, 2013, vol.93, no. 9, pp. 2586-2593. @ ScienceDirect
Sequential Monte Carlo methods (with emphasis on Resampling for particle filtering):
J.12) X. Wang*, C. Li, T. Li*, Y. Liang, Z. Ding, Q. Pan, Variational Bayesian inference for the identification of FIR systems via quantized output data, Automatica（regular paper）132 (2021) 109827
J.11) Wang, X.; Li, T.; Sun, S.; Corchado, J.M. A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking. Sensors 2017, 17, 2707. open access @ MDPI
J.10) T. Li, S. Sun, J. M. Corchado, T. P. Sattar and S. Si, Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter, International Journal of Adapt Control and Signal Processing, 2016, vol. 30, no. 11, pp. 1583–1602. @Wiley
J.9) T. Li, H. Fan, S. Sun, Particle Filtering: Theory, Approach and Application for Multitarget Tracking, Acta Automatica Sinica, 2015, vol.41, no.12, 1981-2002. @ 自动化学报
J.8) T. Li, M. Bolic, P. Djuric, Resampling methods for particle filtering: Classification, implementation, and strategies, IEEE Signal Processing Magazine, 2015, vol.32, no.3, pp. 70-86. @ IEEE Xplore
J.7) T. Li, G. Villarrubia, S. Sun, J. M. Corchado, J. Bajo. Resampling methods for particle filtering: identical distribution, a new method and comparable study, Frontiers of Information Technology & Electronic Engineering, 2015 vol.16, no.11, pp.969-984 @ Springer
J.6) T. Li, S. Sun, T. P. Sattar, and J. M. Corchado. Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches, Expert Systems With Applications, 2014, vol.41, no. 8, pp. 3944-3954. @ScienceDirect
J.5) T. Li, S. Sun, T. P. Sattar, Adapting sample size in particle filters through KLD-resampling, Electronics Letters, 2013, vol.46, no.12, pp.740-742. @ IEEE Xplore
J.4) T. Li, T. P. Sattar, S. Sun, Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters, Signal Processing, 2012, vol.92, no.7, pp. 1637-1645. @ScienceDirect
J.3) T. Li, S. Sun, Y. Gao, Localization of mobile robot using discrete space particle filter. Journal of Mechanical Engineering, 2010, vol.46, no.19, pp.38-43.
J.2) T. Li, S. Sun, etc. Particle merging resampling based Monte Carlo localization for mobile robot. Robot, 2010, vol. 32, no.5, pp. 674-680.
J.1) T. Li, S. Sun, Double-resampling based Monte Carlo localization for mobile robot. Acta Automatica Sinica, 2010, vol. 35, no.9, pp. 1279-1286. @ pdf
c.9) Z. Hu, Y. Xin, D. Li and T. Li*, "Monte Carlo WLS Fuser for Nonlinear/Non-Gaussian State Estimation," 2021 International Conference on Control, Automation and Information Sciences (ICCAIS), 2021, pp. 898-903
c.8) Z. Hu and T. Li*, "A Particle Bernoulli Filter Based on Gaussian Process Learning for Maneuvering Target Tracking," 2022 30th European Signal Processing Conference (EUSIPCO), 2022, pp. 777-781
C.7) T. Li, J. Prieto, J. M. Corchado, A Short Revisit of Nonlinear Gaussian Filters: State-of-the-art and Some Concerns, 16th IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB 2016), 16-19 October 2016, Nanjing, China.
C.6) T. Li, S. Rodriguez, J. Bajo, J.M. Corchado, S. Sun,On the bias of the SIR filter in parameter estimation of the dynamics process of state space models, 12th International Conference Distributed Computing and Artificial Intelligence, pp 87-95, 2015. @ Springer
C.5) Q. Han, T. Li, S. Sun, G. Villarrubia, F. de la Prieta, “1-N” Leader-Follower Formation Control of Multiple Agents Based on Bearing-only Observation, PAAMS 2015, Salamanca Spain, 3-4th June 2015.
C.4) H. Lang, T. Li, G. Villarrubia, S. Sun, J. Bajo, An adaptive particle filter for indoor robot localization, ISamI 2015, Salamanca Spain, 3-4th June 2015.
C.3) R. Zhi, T. Li, M.F. Siyau, S. Sun, Applied technology in adapting the number of particles while maintaining the diversity in the particle filter, Advanced Materials Research Vol. 951 (2014) pp 202-207.
C.2) T. Li, T. P. Sattar, D. Tang, A fast resampling scheme for particle filters, CIWSP'2013, Imperial College, London UK, Jan. 25, 2013.
C.1) T. Li, S. Sun, J. Duan, Monte Carlo localization for mobile robot using adaptive particle merging and splitting technique, IEEE ICIA 2010, Harbin, China, Jun. 20-23, 2010.
Kalman Filter:
T. Li, J. Su, W. Liu and J. M. Corchado, Approximate Gaussian Conjugacy: Parametric Recursive Filtering under Nonlinearity, Multimodality, Uncertainty, and Constraint, and Beyond, Frontiers of Information Technology & Electronic Engineering, 2017, Volume 18, Issue 12, pp 1913–1939, FREE ACCESS ＬＩＮＫ @myResearchGate
C.1) L. Chang, R. Zhi, T. Li, J. M. Corchado, Adaptive M-Estimation for Robust Cubature Kalman Filtering, Sensor Signal Processing for Defence (SSPD), 2016 , Edinburgh, United Kingdom, 2016, pp. 1-5. @ IEEE Xplore.
Published online (non-peer reviewed):
1) T. Li. A gap between simulation and practice for recursive filters: On the state transition noise. arXiv:1308.1056
2) T. Li, J. M. Corchado, J. Bajo, S. Sun and J. F. Paz, Do we always need a filter? arXiv 1408.4636
3) T. Li. The Optimal Arbitrary-Proportional Finite-Set-Partitioning. arXiv:1411.6529
4) T. Li. Return on citation: a consistent metric to evaluate papers, journals and researchers. arXiv:1412.8420
5) T. Li, Distributed SMC-PHD Fusion for Partial, Arithmetic Average Consensus, 2017, arXiv:1712.06128 [cs.SY].
Information Fusion and Data Management (a hybrid subset):
J.9) Y. Song, Z. Hu, T. Li*, H. Fan, Performance Evaluation Metrics and Approaches for Target Tracking: A Survey. Sensors 2022, 22, 793.
J.8) Q Han, F Cao, P Yi, T. Li*, Motion Control of a Gecko-like Robot Based on a Central Pattern Generator, Sensors 21 (18), 6045, 2021
J.7) Liu, R.; Fan, H.; T.Li*; Xiao, H. A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking. Sensors 2019, 19, 4226.
J.6) Z. Wu, S. Yu, T.Li*, A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling, Mathematics, Vol.7, no.6, article no. 529, 2019.
J.5) S. Xiang, T. Li, W. Chen, H. Fan, Q. Fu, Maximal admissible mode decision delay in terminal guidance, Chinese Journal of Aeronautics, vol.32, no.8, 2019, pp.1959-1966.
J.4) J. M. Corchado, Li Weigang, J. Bajo, F. Wu, T. Li*, Special issue on distributed computing and artificial intelligence, Front. Inform. Technol. Electron. Eng. , 2016 17(4):281-282.
J.3) T. Li*, S. Sun, Y. Gao. Fan-shaped grid based global path planning for mobile robot. Robot, 2010, vol.32, no.4, pp. 547-552
J.2) T. Li*, E. Yang, Optimization with “breakage of bar” method in calculating the degree-of-freedom of spatial mechanisms. Applied science and technology, 2008, vol.35, no.8, pp.52-54.
J.1) E. Yang, T. Li*, Y. Nan, A new method for calculating the degree of freedom of planar mechanisms. Applied science and technology, 2007, vol. 34, no.7, pp. 61-64.
C.5) M. Siyau, T. Li*, J. Preito, J. Corchado, J. Bajo, A novel pilot expansion approach for MIMO channel estimation and tracking, IEEE ICUWB 2015, Montreal, Canada, October 4-7, 2015.
C.4) A. Barriuso, F. De la Prieta, T. Li, An agent-based social simulation platform with 3D representation for labor integration of disabled people, PAAMS'15 Salamanca, Spain, 3-5 June, 2015.
C.3) S. Rodríguez, C. Zato, T. Li and J. M. Corchado, Fusion system based on multi-agent systems to merge data from WSN, FUSION 2014, Salamanca, Spain, July 7-10, 2014.
C.2) J. F. De Paz Santana, G. Villarrubia, J. Bajo, G. Sirvent and T. Li, Indoor Location System for Security Guards in Subway Stations, 12th International Conference on Practical Applications of Agents and Multi-Agent Systems(PAAMS'14) Salamanca, Spain, 4-6 June, 2014.
C.1) Á. Lozano, A. Belén Gil, T. Li: Integration of Different ERP Systems on Mobile Devices. PAAMS (Special Sessions) 2014: 27-35.