Dual-satellite source geolocation with time and frequency offsets and satellite location errors

Liu, Chao; Yang, Le; Mihaylova, Lyudmila

doi:10.23919/icif.2017.8009716

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…In this case, the measurements are TDOAs and FDOAs subject to independent random noises when they are estimated using the maximum likelihood (ML) method such as the one from [30]. Outlying measurements can occur due to e.g., temporary time and frequency offsets [31] but they are still independent, since they are from different sources of errors.…”

Section: Problem Formulationmentioning

confidence: 99%

Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

Liu

Xue³

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system dynamic and measurement models through accounting for their impacts when updating the state estimate and covariance. However, the performance of the SKF may break down when the measurements are subject to non-Gaussian and heavy-tail noise. To address this, we impose the Wishart prior distribution on the precision matrix of measurement noise, such that the measurement likelihood now has heavier tails than the Gaussian distribution to deal with the potential occurrence of outliers. Variational inference is invoked to establish analytically tractable methods for computing the posterior of the system state, system biases, and the measurement noise precision matrix. The principle ofn the SKF considers the effect of system biases but does not actively estimate them when two variants of outlier-robust SKFs are incorporated. We evaluate their performance in terms of estimation accuracy and filtering consistency using simulations and real-world data. Promising results are obtained.

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Section: Problem Formulationmentioning

confidence: 99%

Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

Liu

Xue³

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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“…Among the positioning technologies, the Global Navigation Satellite System (GNSS) is well-known but only efficient in open outdoor environments. Alternatively, in wireless sensor networks (WSNs), the location of the object can be found by observing the signal from wireless devices equipped on the target [6][7][8]. For fine-grained positioning, commonly used parameters are the time of arrival (ToA), time difference of arrival (TDoA), and direction of arrival (DoA), to just list a few.…”

Section: Related Workmentioning

confidence: 99%

Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station

Zhou

Yao²,

Yang

et al. 2019

Electronics

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Rigid body localization (RBL) is to simultaneously estimate the position and attitude of a rigid target. In this paper, we focus on the RBL problem using a single base station (BS) and direction of arrival (DoA) measurements. Several wireless sensors are mounted on the rigid body of interest, and their topology information is known a priori. The single BS measures the DoAs of wireless sensor signals and fuses them with the sensor topology information to estimate the position and orientation of the rigid body and achieve RBL. We propose two RBL methods, namely, the observation matching (OM) algorithm and topology matching (TM) algorithm with refinement. The emerging participatory searching algorithm (PSA) is adopted in both methods to solve the nonlinear matching problems. Simulations show that, compared with the existing approach, the OM method can achieve better RBL accuracy under high DoA noise levels, while the performance of the TM algorithm with refinement is closer to the constrained Cramér–Rao bound (CCRB) under low DoA noise levels.

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“…Тime Difference of Arrival, временная разность прибытия) либо разностно-доплеровским (FDOA, англ. Frequency Difference of Arrival, частотная разность прибытия) [1][2][3] методом на основании оценки временной либо частотной разности прихода сигналов, принятых с нескольких СР, ретранслирующих сигналы основного и боковых лепестков диаграммы направленности антенны ИРИ [4][5][6][7].…”

Section: Introductionunclassified

Compensation of relay satellites heterodyne instability for locating terrestrial radio emission sources

Gall¹,

Shevchenko²,

Malyshev³

2021

Proceedings of the RHEAS

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Unintentional and intentional interference of terrestrial radio sources operating via geostationary relay satellites to legal users of satellite communication systems requires accurate determination of their location. Methods of terrestrial radio sources location are based on the calculation of an cross-аambiguity function by additive mixtures of signals and noise received from relay satellites. In the presence of frequency-phase instability of relay satellites heterodynes the retransmitted signals have phase distortions, which lead to a decrease in the signal-to-noise ratio (SNR) when calculating the cross- ambiguity function. The paper is aimed to study the effect of phase distortions caused by the instability of relay satellites heterodynes on SNR at the correlator output and to develop methods for their compensation based on statistical radio engineering and digital signal processing. The study of the proposed compensation methods was carried out by statistical simulation modeling. The SNR dependences at the correlator output on the duration of correlated signals for the model with a domi-nant frequency noise and frequency random walk have been obtained and a method for compensat-ing phase distortions caused by the instability of the relay satellites heterodynes has been developed. The energy gain has been estimated by applying the proposed compensation method. It has been shown that the developed method of compensation of relay satellites heterodynes instability allows achieving a significant gain in the SNR at the correlator output and contributes to increasing the probability of radio source signal detection from auxiliary relay satellites.

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Dual-satellite source geolocation with time and frequency offsets and satellite location errors

Cited by 11 publications

References 31 publications

Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station

Compensation of relay satellites heterodyne instability for locating terrestrial radio emission sources

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