Gaussian message passing-based cooperative localization with node selection scheme in wireless networks

Liu, Yangyang; Lian, Baowang; Zhou, Tong

doi:10.1016/j.sigpro.2018.10.023

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…e satellite navigation system has become one of the most important technical means in modern navigation and positioning measurement [1,2]. To pursue more accurate positioning results, it is necessary to analyze the reasons that affect the accuracy of a satellite navigation system and take targeted measures to reduce and correct the relevant errors and obtain a better positioning performance [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…ere are many factors which will affect the positioning error of satellite navigation, among which ionospheric error and tropospheric error can be eliminated by differential technique or some mathematical methods [5,6]. However, the multipath error is affected by many aspects, and it is difficult to establish a more accurate model for it and eliminate it by the general method, so multipath error has become the main error source that affects satellite navigation [7,8]. As early as the GPS demonstration test, the influence of the multipath effect on satellite ranging performance and positioning results was considered by the designers.…”

Section: Introductionmentioning

confidence: 99%

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A Multipath Processing Technology Based on Multiparameter-Combined Observation in GNSS

Zhou

Lian

2021

Mobile Information Systems

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In the Global Navigation Satellite System (GNSS), the multipath error affected by many aspects is the main error source that affects satellite navigation, and it is difficult to establish a much more accurate model to analyze it. Based on the ground multipath reflection model, it firstly deeply studies the influence of GNSS satellite orbit parameters on multipath fading frequency and establishes a multipath signal model related to satellite orbit parameters. Secondly, the influence of carrier phase cycle slip, receiver clock adjustment, and GNSS satellite orbit on multiparameter- (MP-) combined observations is analyzed in detail based on the measured data. Finally, aiming at the common phenomenon of code-carrier divergence in the Beidou system, the elevation-based pseudorange correction model and a sidereal filtering are built to correct the MP errors; experiments with measured data show that there is a fluctuation range reduction of 35.7% after sidereal filtering when the receiver reaches a steady state.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multipath Processing Technology Based on Multiparameter-Combined Observation in GNSS

Zhou

Lian

2021

Mobile Information Systems

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“…The UWB positioning technology based on time difference of arrival (TDOA) can realize the positioning accuracy of centimeter level. Compared with WIFI, ZigBee, Bluetooth, and other wireless positioning technology, UWB positioning system has the advantages of strong penetration, anti‐jamming and high positioning accuracy, so it has significant advantages in positioning, tracking and navigation of indoor moving targets 5 . Meanwhile, UWB technology is easy to combine positioning with communication, the rapid development of short distance UWB communication will push forward the UWB positioning technology.…”

Section: Introductionmentioning

confidence: 99%

Research on Indoor UWB Positioning Based on Expectation Maximization in NLOS Environment

Zhou

Xiao

Liu

et al. 2021

Concurrency and Computation

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Summary Ultra‐wide band (UWB) technology is especially suitable for indoor positioning because of its strong anti‐interference ability, it can achieve centimeter‐level positioning accuracy in the line of sight (LOS) environment, but it is challenging to construct a robust and high‐precision algorithm in indoor complex and changeable environments. Therefore, it establishes an improved standard Kalman filter (SKF) based on expectation maximization (SKF‐EM), which models the prediction error covariance and measurement noise using the maximum likelihood criterion, estimates the filter parameters online through the expectation maximum theory, and updates the Kalman filter gain according to the Kalman filter based on dropped measurements method (SKF‐DMM). Experimental results show that: in case of serious occlusion, the maximum positioning error of SKF‐EM algorithm is 1.03 m, 39% lower than that of SKF and 60.2% lower than that of UWB.

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“…Simulation results demonstrate that the improved Channel-SLAM algorithm has higher positioning reliability and lower computational complexity than the original method. A multipath-assisted SLAM algorithm called BP-SLAM based on belief propagation (BP) [24] was proposed in [25]. The approach jointly realizes the probabilistic DA and the estimation of UE positions and feature positions in the environment map.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, due to the influence of electromagnetic interference and surrounding environments, the detection probability is usually variable. However, the influence of the detection probability was not considered or simply considered to be fixed and known in [22][23][24][25]. Adaptive algorithms of the time-varying detection probabilitybased BP-SLAM algorithm were proposed in [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity

2021

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In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.

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Gaussian message passing-based cooperative localization with node selection scheme in wireless networks

Cited by 20 publications

References 30 publications

A Multipath Processing Technology Based on Multiparameter-Combined Observation in GNSS

A Multipath Processing Technology Based on Multiparameter-Combined Observation in GNSS

Research on Indoor UWB Positioning Based on Expectation Maximization in NLOS Environment

Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity

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