Selected Publications

O2:  C4F/F4S/FoT: Data-driven HMM-free approaches for Detection, Tracking and Forecasting (DTF)

• 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 

• A sensor selection approach to maneuvering target tracking based on trajectory function of time. EURASIP J. Adv. Signal Process. 2022, 72 (2022) 

• Target Tracking with Equality/Inequality Constraints Based on Trajectory Function of Time, IEEE Signal Processing Letters, , vol. 28, pp. 1330-1334, 2021

• Single-road-constrained positioning based on deterministic trajectory geometry, IEEE Communications Letters, vol.23, no.1, pp. 80-83, 2019.  @ IEEEXplore

• 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 Extended Pre-print @ arXiv:1708.02196 [stat.AP] 

• Distributed Flooding-then-Clustering: A Lazy Networking Approach for Distributed Multiple Target Tracking, FUSION 2018, Cambridge, UK, July 10-13 2018. @IEEE Xplore 

• Effectiveness of Bayesian Filters: An Information Fusion Perspective, Information Sciences, 2016, 329: 670-689.  PDF  @  ScienceDirect 

• Multi-source Homogeneous Data Clustering for Multi-target Detection from Cluttered Background with Misdetection, Applied Soft Computing 60 (2017) 436–446 @ ScienceDirect

Sensor Networking, AA-fusion, Distributed Tracking:

• 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  

• 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 

• Arithmetic Average Density Fusion Part I: Some Statistic and Information-theoretic Results.  Information fusion, 2024, 104, 102199 @sciencedirect 

• Finite mixture modeling in time series: A survey of Bayesian filters and fusion approaches, Information Fusion, 2023, 98: 101827 @ScienceDirect 

• Multi-sensor Suboptimal Fusion Student's t Filter, IEEE T-AES, vol. 59, no. 3, pp. 3378-3387, June 2023. @IEEE Xplore

• Best Fit of Mixture for Multi-Sensor Poisson Multi-Bernoulli Mixture Filtering, Signal Process., Vol. 202, 2023, 108739 @Sciencedirect 

• A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters, Information Fusion, 73: 111-124, 2021. ScienceDirect 

• 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

• On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking, IEEE Transactions on Signal Processing, 68(1):2883-2896, 2020, IEEE eXplore 

• A Distributed Particle-PHD Filter Using Arithmetic-Average Fusion of Gaussian Mixture Parameters, Dec. 2018, preprint arXiv:1712.06128 [cs.SY]. submitted to Inform. Fusion.

• Convergence of distributed flooding and its application for distributed Bayesian filtering, IEEE Trans. Signal and Information Processing over Networks, vol.3, no.3, pp. 580 - 591. @ IEEE Xplore

• 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

• 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.  @ Elsevier

• 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

• 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 

Monte Carlo methods (with emphasis on Resampling for particle filtering):

• Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter, Int. J. Adapt Control and Signal Processing, 2016; vol. 30, no.11, pp.1583–1602. @Wiley

• Resampling methods for particle filtering: Classification, implementation, and strategies, IEEE Signal Processing Magazine, 2015, vol.32, no.3, pp. 70-86.   @ IEEE Xplore

• Resampling methods for particle filtering: identical distribution, a new method and comparable study, FITEE, 2015 vol.16, no.11, pp.969-984, invited paper    @ Springer 

• Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches, Expert Sys. App., 2014, vol.41, no. 8, pp. 3944-3954.     @ScienceDirect

• Adapting sample size in particle filters through KLD-resampling, Electronics Letters, 2013, vol.46, no.12, pp.740-742.             @ IEEE Xplore

• Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters, Signal Processing, 2012, vol.92, no.7, pp. 1637-1645.         @ScienceDirect

• Variational Bayesian inference for the identification of FIR systems via quantized output data, Automatica, 132 (2021) 109827, Regular paper.

Kalman filter:

• Approximate Gaussian Conjugacy: Parametric Recursive Filtering under Nonlinearity, Multimodality, Uncertainty, and Constraint, and Beyond, Frontiers of Information Technology & Electronic Engineering, 2017, 18(12):1913-1939,   ＬＩＮＫ   Best paper award of 2017!