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distributed bayesian vector estimation using noise-optimized low-resolution sensor observations

DIGITAL SIGNAL PROCESSING 2021年 118卷

作者： Liu, Jia Duan, Fabing Chapeau-Blondeau, Francois Abbott, Derek Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China Univ Angers Lab Angevin Rech Ingn Syst LARIS 62 Ave Notre Dame Lac F-49000 Angers France Univ Adelaide Ctr Biomed Engn CBME Adelaide SA 5005 Australia Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia

The distributed bayesian vector parameter estimation problem based on low-resolution observations is investigated in a network, where each node represents an ensemble of estimates from a large number of sensors. A noise-enhanced bayesian vector estimator that benefits from artificially added noise is proposed. For a network node composed of a sufficiently large number of identical low-resolution sensors, a lemma governing the weight coefficients is proven, and low-cost calculation expressions of the designed estimator and its bayesian mean square error (MSE) are derived by avoiding costly computations due to high-dimensional matrix inversions. Experimental results show that by intentionally adding an appropriate amount of noise to networks of the low-resolution sensors, the MSE of the designed bayesian vector estimator can be significantly reduced. (C) 2021 Elsevier Inc. All rights reserved.

关键词： distributed bayesian estimation Low-resolution sensor Optimal added noise Noise-enhanced vector estimator Stochastic resonance

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Asymptotic optimal quantizer design for distributed bayesian estimation 41

Asymptotic optimal quantizer design for distributed Bayesian...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

作者： Li, Xia Quo, Jun Rogers, Uri Chen, Hao Electrical and Computer Engineering Boise State University United States Engineering Eastern Washington University United States

ISBN: (纸本)9781479999880

In this paper, we address the optimal quantizer design problem for distributed bayesian parameter estimation with one-bit quantization at local sensors. A performance limit obtained for any distributed parameter estimator with a known prior is adopted as a guidance for quantizer design. Aided by the performance limit, the optimal quantizer and a set of noisy observation models that achieve the performance limit are derived. Further, when the performance limit may not be achievable for some applications, we develop a near-optimal estimator which consists of a dithered noise and a single threshold quantizer. In the scenario where the parameter is Gaussian and signal-to-noise ratio is greater than -1.138 dB, we show that one can construct such an estimator that achieves approximately 99.65% of the performance limit. © 2016 IEEE.

关键词： Asymptotic Performance Limit Cramer-Rao Lower Bound distributed bayesian estimation One-bit Quantization Quantizer Design

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ASYMPTOTIC OPTIMAL QUANTIZER DESIGN FOR distributed bayesian estimation

ASYMPTOTIC OPTIMAL QUANTIZER DESIGN FOR DISTRIBUTED BAYESIAN...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xia Li Jun Guo Uri Rogers Hao Chen Electrical and Computer Engineering Boise State University Engineering Eastern Washington University

ISBN: (纸本)9781479999897

关键词： distributed bayesian estimation One-bit Quantization Quantizer Design Cramer-Rao Lower Bound Asymptotic Performance Limit

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THE PERFORMANCE LIMIT FOR distributed bayesian estimation WITH IDENTICAL ONE-BIT QUANTIZERS

THE PERFORMANCE LIMIT FOR DISTRIBUTED BAYESIAN ESTIMATION WI...

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2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

作者： Li, Xia Guo, Jun Chen, Hao Rogers, Uri Boise State Univ Dept Elect & Comp Engn Boise ID 83712 USA Eastern Washington Univ Dept Elect Engn Cheney WA 99004 USA

ISBN: (纸本)9781467391696

In this paper, a performance limit is derived for a distributed bayesian parameter estimation problem in sensor networks where the prior probability density function of the parameter is known. The sensor observations are assumed conditionally independent and identically distributed given the parameter to be estimated, and the sensors employ independent and identical quantizers. The performance limit is established in terms of the best possible asymptotic performance that a distributed estimation scheme can achieve for all possible sensor observation models. This performance limit is obtained by deriving the optimal probabilistic quantizer under the ideal setting, where the sensors observe the parameter directly without any noise or distortion. With a uniform prior, the derived bayesian performance limit and the associated quantizer are the same as the previous developed performance limit and quantizers under the minimax framework, where the parameter is assumed to be fixed but unknown. This proposed performance limit under distributed bayesian setting is compared against a widely used performance bound that is based on full-precision sensor observations. This comparison shows that the performance limit derived in this paper is comparatively much tighter in most meaningful signal-to-noise ratio (SNR) regions. Moreover, unlike the unquantized observations performance limit which can never be achieved, this performance limit can be achieved under certain noise observation models.

关键词： distributed bayesian estimation 1-bit Quantization Cramer-Rao Lower Bound Asymptotic Performance Limit

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Cooperative-Based Augmentation System Using bayesian Filtering

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第7期73卷 10502-10515页

作者： Silva, Paulo R. P. Bruno, Marcelo G. S. Moraes, Alison O. Inst Tecnol Aeronaut BR-12228900 Sao Jose Dos Campos Brazil Inst Aeronaut & Espaco BR-12228904 Sao Jose Dos Campos Brazil

We introduce in this paper a cooperative-based augmentation system for aircraft positioning. To that end, we derive a distributed estimation algorithm for cooperative localization over a network of aircraft that are partially connected by communication links and are also connected to ground-based anchor nodes. The proposed framework assumes that only a subset of the aircraft may have access to GPS measurements to perform local state estimation, and that the different aircraft exchange messages with neighboring nodes on the network to improve their own state estimates. Simulation results show that cooperation significantly reduces the average 3D root mean-square localization error when part of the aircraft are flying dead-reckoning and that the average vertical error cumulative distribution function remains below 4 meters for 95% of time when all aircraft have access to GPS measurements.

关键词： Global Positioning System Aircraft Position measurement Sum product algorithm Location awareness Three-dimensional displays Peer-to-peer computing Cooperative positioning distributed bayesian estimation factor graphs sum-product algorithm global navigation satellite system sequential Monte Carlo

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Cooperative Localization under Ionospheric Scintillation Events

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 1-24页

作者： Silva, Paulo R. P. Bruno, Marcelo G. S. Moraes, Alison O. Inst Tecnol Aeronaut BR-12228900 Sao Jose Dos Campos SP Brazil Inst Aeronaut & Espaco BR-12228904 Sao Jose Dos Campos SP Brazil

Ionospheric scintillation causes major impairments to Global Navigation Satellite System (GNSS) in low-latitude regions. In severe scenarios, this event can lead to complete loss of lock, thus making GNSS measurements unusable for navigation. In this paper, we derive a cooperative localization algorithm where a set of partially connected aircraft exchange messages with neighboring nodes on the network to improve their own position estimates. We consider the scintillation events as abrupt changes in the measurement variance, which are modeled by a discrete-valued Markov process at the nodes which have access to GNSS measurements. Simulation results show that Markovian modeling and cooperation via factor graph message passing reduce the average 3D root mean square localization error and yield an average vertical position error that meets civil aviation standards for approach and landing.

关键词： Cooperative positioning distributed bayesian estimation Sequential Monte Carlo Sum-product algorithm Global navigation satellite system Discrete-time Markov process

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GNSS-Denied Joint Cooperative Terrain Navigation and Target Tracking Using Factor Graph Geometric Average Fusion

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 991-1005页

作者： Oliveira, Hallysson Dias, Stiven S. Bruno, Marcelo G. S. Inst Tecnol Aeronaut BR-12228900 Sao Jose Dos Campos Brazil Embraer SA BR-12247016 Sao Jose Dos Campos Brazil Eindhoven Univ Technol NL-5600 Eindhoven Netherlands

We propose a fully distributed methodology based on factor graphs for joint cooperative localization and distributed noncooperative target tracking in a 3-D scenario where multiple surveillance aircraft fly in formation without access to global navigation satellite system (GNSS) measurements or communication with anchor nodes. Our approach is based on the adapt-then-combine (ATC) diffusion scheme, which is integrated into the factor graph by the introduction of special combine factors to perform geometric average fusion of the target beliefs over a partially connected network. The updated target belief held by each aircraft following the combine step is also fed back to improve the aircraft's own self-localization, assimilating the target measurements. Simulation results show that the proposed distributed algorithm performed close to the posterior Cramer-Rao lower bound of the optimal centralized solution and that the agents approached a consensus about the target state estimate.

关键词： Aircraft Target tracking Three-dimensional displays Location awareness Radar tracking Aircraft navigation Global navigation satellite system Cooperative localization distributed bayesian estimation distributed target tracking factor graphs geometric average fusion

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Cooperative Terrain Navigation Using Hybrid GMM/SMC Message Passing on Factor Graphs

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第5期56卷 3958-3970页

作者： Oliveira, Hallysson Dias, Stiven Schwanz Bruno, Marcelo Gomes da Silva Embraer SA BR-12247820 Sao Jose Dos Campos SP Brazil Inst Tecnol Aeronaut BR-12228900 Sao Jose Dos Campos SP Brazil

We introduce in this article a distributed factor-graph-based algorithm for anchorless cooperative aircraft localization in a GNSS-denied scenario without fixed infrastructure. The agents use terrain-aided navigation (TAN) to perform local position estimation and exchange messages to improve their position beliefs. Internode communication cost is reduced using a hybrid Gaussian mixture model / sequential Monte Carlo (GMM/SMC) approach. Simulation results show that, even in a partially connected network where only a small part of the agents perform TAN, cooperation yields better results than all aircraft performing TAN independently.

关键词： Aircraft navigation Aircraft Sum product algorithm Random variables Monte Carlo methods Message passing Gaussian mixture model Cooperative localization distributed bayesian estimation factor graphs Gaussian mixture models sequential Monte Carlo sum-product algorithm terrain aided navigation

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