Adaptive finite-horizon group estimation for networked navigation systems with remote sensing complementary observations under mixed LOS/NLOS environments
type: Item
instance of: scholarly article
title: Adaptive finite-horizon group estimation for networked navigation systems with remote sensing complementary observations under mixed LOS/NLOS environments
zbMATH Open document ID: 1400.93298
DOI: 10.1155/2016/6489165
author: Chao Gao
author: Jianhua Lu
author: Guorong Zhao
author: Shuang Pan
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 6953452
Wikidata QID: Q59140891
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/6489165
OpenAlex ID: W2566693320
cites work: EM- and JMAP-ML Based Joint Estimation Algorithms for Robust Wireless Geolocation in Mixed LOS/NLOS Environments
cites work: Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions
cites work: Moving Horizon State Estimation for Networked Control Systems With Multiple Packet Dropouts
cites work: A Moving Horizon Approach to Networked Control System Design
cites work: A receding horizon Kalman FIR filter for discrete time-invariant systems
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description: scientific article; zbMATH DE number 6953452