Nonlinear Filtering with IMM Algorithm for Coastal Radar Target Tracking System
Abstract: This paper presents
a performance evaluation of nonlinear filtering with Interacting Multiple Model
(IMM) algorithm for implementation on Indonesian coastal radar target tracking
system. On thisradar, target motion is modeled using Cartesian coordinate but
target position measurements are provided in polar coordinate (range and
azimuth). For this implementation, we investigated two types of nonlinear filtering,
Converted Measurement Kalman Filter (CMKF) and Unscented Kalman Filter (UKF).
IMM algorithm is used to anticipate target motion uncertainty. Many simulations
on radar target tracking are developed under assumption that noise
characteristic is known. In this paper, the performance of IMMCMKF and IMM-UKF
is evaluated for condition that radar doesn’t know noise characteristic and
there is mismatch on noise modeling. Results from simulation show that IMM-CMKF
has better performance than IMM-UKF when tracking maneuvering trajectory.
Results also show that IMM-CMKF is more robust than IMM-UKF when there is mismatch
on noise modeling.
Author: Rika Sustika, Joko
Suryana
Journal Code: jptkomputergg150030
