Bias Estimation in Sensor Networks

Autor: Pietro Tesi, Nima Monshizadeh, Claudio De Persis, Mingming Shi
Přispěvatelé: UCL - SST/ICTM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Smart Manufacturing Systems
Rok vydání: 2020
Předmět:
0209 industrial biotechnology
Control and Optimization
Correctness
Linear programming
Computer Networks and Communications
Computer science
Systems and Control (eess.SY)
02 engineering and technology
Network topology
Matrix decomposition
020901 industrial engineering & automation
SYSTEMS
FOS: Electrical engineering
electronic engineering
information engineering
FOS: Mathematics
0202 electrical engineering
electronic engineering
information engineering
ALGORITHM
wireless sensor networks
EQUATIONS
Mathematics - Optimization and Control
compressed sensing
Bipartite graph
Observational error
estimation
STATE ESTIMATION
linear programming
LOCALIZATION
020206 networking & telecommunications
DISTRIBUTED ESTIMATION
Compressed sensing
Optimization and Control (math.OC)
Control and Systems Engineering
Signal Processing
Computer Science - Systems and Control
Algorithm
Wireless sensor network
Zdroj: IEEE Transactions on Control of Network Systems, Vol. 7, no.3, p. 1534-1546 (2020)
IEEE Transactions on Control of Network Systems, 7(3), 1534-1546. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ISSN: 2372-2533
2325-5870
DOI: 10.1109/tcns.2020.2984684
Popis: This paper investigates the problem of estimating biases affecting relative state measurements in a sensor network. Each sensor measures the relative states of its neighbors and this measurement is corrupted by a constant bias. We analyse under what conditions on the network topology and the maximum number of biased sensors the biases can be correctly estimated. We show that for non-bipartite graphs the biases can always be determined even when all the sensors are corrupted, while for bipartite graphs more than half of the sensors should be unbiased to ensure the correctness of the bias estimation. If the biases are heterogeneous, then the number of unbiased sensors can be reduced to two. Based on these conditions, we propose some algorithms to estimate the biases.
12 pages, 8 figures
Databáze: OpenAIRE
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