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Rika Sustika
Indonesian Institute of Sciences (LIPI)
Computer Science & IT

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Nonlinear-Filtering with Interacting Multiple-Model Algorithm for Coastal Radar Target Tracking System Rika Sustika; Joko Suryana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 1: March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i1.791

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 this radar, 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 IMM-CMKF 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.
Compressive Sensing Algorithm for Data Compression on Weather Monitoring System Rika Sustika; Bambang Sugiarto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3021

Abstract

Compressive sensing (CS) is new data acquisition algorithm that can be used for compression. CS theory certifies that signals can be recovered from far fewer samples or measurements than Nyquist rate. On this paper, the compressive sensing technique is applied for data compression on our weather monitoring system. On this weather monitoring system, compression using compressive sensing with fewer samples or measurements means minimizing sensing and overall energy cost. Our focus on this paper lies in the selection of matrix for representation basis under which the weather data are sparsely represented. We evaluated three types of representation basis using data from real measurement. By comparing performance of data recovery, result show that DCT (Discrete Cosine Transform) is the best performance on sparsifying weather data
Co-Authors Bambang Sugiarto Joko Suryana