Paper Info
Title
System-level performance analysis for Bayesian cooperative positioning: From global to local
Abstract
Cooperative positioning (CP) can be used either to calibrate the accumulated error from inertial navigation or as a stand-alone navigation system. Though intensive research has been conducted on CP, there is a need to further investigate the joint impact from the system level on the accuracy. We derive a posterior Cramér-Rao bound (PCRB) considering both the physical layer (PHY) signal structure and the asynchronous latency from the multiple access control layer (MAC). The PCRB shows an immediate relationship between the theoretical accuracy limit and the effective factors, e.g. geometry, node dynamic, latency, signal structure, power, etc. which is useful to assess a cooperative system. However, for a large-scale decentralized cooperation network, calculating the PCRB becomes difficult due to the high state dimension and the absence of global information. We propose an equivalent ranging variance (ERV) scheme which projects the neighbor's positioning uncertainty to the distance measurement inaccuracy. With this scheme, the interaction among the mobile terminals (MTs), e.g. measurement and communication can be decoupled. We use the ERV to derive a local PCRB (L-PCRB) which approximates the PCRB locally at each MT with low complexity. We further propose combining the ERV and L-PCRB together to improve the precision of the Bayesian localization algorithms. Simulation with an L-PCRB-aided distributed particle filter (DPF) in two typical cooperative positioning scenarios show a significant improvement comparing with the non-cooperative or standard DPF.
Year
DOI
Venue
2013
10.1109/IPIN.2013.6817888
Indoor Positioning and Indoor Navigation
Keywords
Field
DocType
Bayes methods,Global Positioning System,particle filtering (numerical methods),Bayesian cooperative positioning,Bayesian localization algorithms,CP,ERV scheme,L-PCRB,MAC,MT,PHY signal structure,asynchronous latency,distance measurement inaccuracy,distributed particle filter,effective factors,equivalent ranging variance scheme,geometry,inertial navigation,large-scale decentralized cooperation network,local PCRB,mobile terminals,multiple access control layer,node dynamic,noncooperative DPF,physical layer signal structure,positioning uncertainty,posterior Cramer-Rao bound,stand-alone navigation system,standard DPF,system-level performance analysis,theoretical accuracy limit
Inertial navigation system,Asynchronous communication,Control theory,Particle filter,Navigation system,Algorithm,Electronic engineering,Ranging,Physical layer,PHY,Engineering,Bayesian probability
Conference
ISSN
Citations 
PageRank 
2162-7347
4
0.51
References 
Authors
9
4
Name
Order
Citations
PageRank
Zhang, S.181.18
Ronald Raulefs219619.66
armin dammann321236.06
Stephan Sand47717.33