264 citations · 1.1k across the 18 of their papers we have counts for
18 papers
Arithmetic Average Density Fusion -- Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation
Tiancheng Li, Haozhe Liang, Guchong Li +2
This paper, the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking, addresses the intricate challenge…
Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion
Tiancheng Li, Ruibo Yan, Kai Da +1
This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (…
Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
Tiancheng Li
As a fundamental information fusion approach, the arithmetic average (AA) fusion has recently been investigated for various random finite set (RFS) filter fusion in the context of…
Multi-sensor Suboptimal Fusion Student's Filter
Tiancheng Li, Zheng Hu, Zhunga Liu +1
A multi-sensor fusion Student's filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an informati…
Arithmetic Average Density Fusion -- Part I: Some Statistic and Information-theoretic Results
Tiancheng Li, Yan Song, Enbin Song +1
Finite mixture such as the Gaussian mixture is a flexible and powerful probabilistic modeling tool for representing the multimodal distribution widely involved in many estimation a…
From Target Tracking to Targeting Track: A Data-Driven Yet Analytical Approach to Joint Target Detection and Tracking
Tiancheng Li, Yan Song, Hongqi Fan
This paper addresses the problem of real-time detection and tracking of a non-cooperative target in the challenging scenario with almost no a-priori information about target birth,…
Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion
Tiancheng Li, Hongqi Fan, Jesús G. Herrero +1
Two fundamental approaches to information averaging are based on linear and logarithmic combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing i…
A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion
Tiancheng Li, Franz Hlawatsch
We propose a particle-based distributed PHD filter for tracking an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors co…
Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion
Tiancheng Li, Juan M Corchado, Shudong Sun
We propose a novel consensus notion, called "partial consensus", for distributed GM-PHD (Gaussian mixture probability hypothesis density) fusion based on a peer-to-peer (P2P) senso…
Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting
Tiancheng Li, Huimin Chen, Shudong Sun +1
We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to…
The Optimal Arbitrary-Proportional Finite-Set-Partitioning
Tiancheng Li
This paper considers the arbitrary-proportional finite-set-partitioning problem which involves partitioning a finite set into multiple subsets with respect to arbitrary nonnegative…
Do we always need a filter?
Tiancheng Li, Juan M. Corchado, Javier Bajo +2
Since the groundbreaking work of the Kalman filter in the 1960s, considerable effort has been devoted to various discrete time filters for dynamic state estimation, especially incl…
Bias of the SIR filter in estimation of the state transition noise
Tiancheng Li
This Note investigates the bias of the sampling importance resampling (SIR) filter in estimation of the state transition noise in the state space model. The SIR filter may suffer f…
A Gap between Simulation and Practice for Recursive Filters: On the State Transition Noise
Tiancheng Li
In order to evaluate and compare different recursive filters, simulation is a common tool and numerous simulation models are widely used as 'benchmark'. In the simulation, the cont…
Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches
Tiancheng Li, Shudong Sun, Tariq P. Sattar +1
During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degener…
Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter
Tiancheng Li, Shudong Sun, Juan M. Corchado +2
The likelihood calculation of a vast number of particles is the computational bottleneck for the particle filter in applications where the observation information is rich. For fast…
Roughening Methods to Prevent Sample Impoverishment in the Particle PHD Filter
Tiancheng Li, Tariq P. Sattar, Qing Han +1
Mahler's PHD (Probability Hypothesis Density) filter and its particle implementation (as called the particle PHD filter) have gained popularity to solve general MTT (Multi-target T…
Adapting sample size in particle filters through KLD-resampling
Tiancheng Li, Shudong Sun, Tariq Pervez Sattar
This letter provides an adaptive resampling method. It determines the number of particles to resample so that the Kullback-Leibler distance (KLD) between distribution of particles…