Maneuvering Target Tracking in the Presence of Glint using the Nonlinear Gaussian Mixture Kalman Filter

2010/01/01 by Igal Bilik, I. Bilik, J. Tabrikian +1 · 5 citations
Computer Science · Engineering · #Target Tracking and Data Fusion in Sensor Networks #Inertial Sensor and Navigation

paper · doi:10.1109/taes.2010.5417160

Abstract

The problem of maneuvering target tracking in the presence of glint noise is addressed in this work. The main challenge in this problem stems from its nonlinearity and non-Gaussianity. A new estimator, named as nonlinear Gaussian mixture Kalman filter (NL-GMKF) is derived based on the minimum-mean-square error (MMSE) criterion and applied to the problem of maneuvering target tracking in the presence of glint. The tracking performance of the NL-GMKF is evaluated and compared with the interacting multiple modeling (IMM) implemented with extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF) and the Gaussian sum PF (GSPF). It is shown that the NL-GMKF outperforms these algorithms in several examples with maneuvering target and/or glint noise measurements.

Citations

• Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
• Survey of maneuvering target tracking. part v: multiple-model methods
• The interacting multiple model algorithm for systems with Markovian switching coefficients
• Survey of maneuvering targettracking . part I: dynamic models

Cited by

• A Sliding Window Variational Outlier-Robust Kalman Filter Based on Student’s t -Noise Modeling
• Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information. [europepmc]
• The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. [europepmc]
• A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise. [europepmc]
• A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise. [europepmc]

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• Maneuvering, Multi-Target Tracking using Particle Filters
• Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
• Two-Filter Adaptive Gaussian Mixture Smoothing for Nonlinear Systems