A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults
Source abstract
Abstract
Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Target Tracking and Data Fusion in Sensor Networks · Inertial Sensor and Navigation · Underwater Acoustics Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an 710038, China
Astronautics College, Northwestern Polytechnic University, Xi’an 710072, China
Funding
National Natural Science Foundation of China · National Aviation Science Foundation of China · Shaanxi Natural Science Foundation of China
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