Survey of maneuvering target tracking. part v: multiple-model methods

2005/10/01 by X Rong Li, V.P. Jilkov, Vesselin P. Jilkov · 16 citations
Computer Science · Engineering · #Target Tracking and Data Fusion in Sensor Networks #Fault Detection and Control Systems #Inertial Sensor and Navigation

paper · doi:10.1109/taes.2005.1561886

Abstract

This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

Citations

• Kalman filter versus IMM estimator: when do we need the latter?
• Maximum likelihood estimates of linear dynamic systems
• Benchmark for radar allocation and tracking in ECM
• The interacting multiple model algorithm for systems with Markovian switching coefficients
• A tutorial on hidden Markov models and selected applications in speech recognition
• Survey of maneuvering targettracking . part I: dynamic models

Cited by

• Maneuvering Target Tracking in the Presence of Glint using the Nonlinear Gaussian Mixture Kalman Filter
• Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill
• Intent Inference and Syntactic Tracking with GMTI Measurements
• Simultaneous Estimation of State and Packet-Loss Occurrences in Networked Control Systems
• Bayesian Nonparametric Dynamical Clustering of Time Series
• Adaptive Lyapunov-constrained MPC for fault-tolerant AUV trajectory tracking
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance
• Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach. [europepmc]
• Robust Interacting Multiple Model Filter Based on Student's t -Distribution for Heavy-Tailed Measurement Noises. [europepmc]
• Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System. [europepmc]
• Architecture for Trajectory-Based Fishing Ship Classification with AIS Data. [europepmc]
• Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning. [europepmc]
• Interactive multi-model fault diagnosis method of switched reluctance motor based on low delay anti-interference. [europepmc]
• RCS-Doppler-Assisted MM-GM-PHD Filter for Passive Radar in Non-Uniform Clutter. [europepmc]
• A Hierarchical Adaptive Moment Matching Multiple Model Tracking Method for Hypersonic Glide Target Under Measurement Uncertainty. [europepmc]
• Multiple model trajectory poisson multi-bernoulli mixtures filter for tracking multiple maneuvering objects. [europepmc]

Related

• Survey of maneuvering targettracking . part I: dynamic models
• Interacting multiple model methods in target tracking: a survey
• Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
• A Message Passing Approach for Multiple Maneuvering Target Tracking
• A Generalized Labeled Multi-Bernoulli Filter for Maneuvering Targets