Research on FOA and TOA Estimation Algorithm of Galileo SAR Signal

Wang, Kun; Dong, Zhihong; Wu, Siliang

doi:10.1109/iciecs.2010.5677685

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…Using these, the problem of selfinterference, such as the multiple path effect of a single signal, is resolved. With an assumption that the message bit width and the carrier frequency offset are unknown, a maximum likelihood estimation algorithm was proposed to estimate TOA and FOA from Galileo-SAR [8]. To overcome the maximum likelihood estimation errors of this method in the search phase, an algorithm, the so-called Barycentre calculation algorithm, was also incorporated.…”

Section: Introductionmentioning

confidence: 99%

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Improved position estimation method for locating an encrypted COSPAS‐SARSAT beacon

Lee

Song

Jeong

et al. 2021

IET Radar Sonar & Navi

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This study addresses a signal processing technique in a search and rescue system. An international disaster rescue communications support programme such as COSPASsearch and rescue satellite-aided tracking (SARSAT) estimates the position of a beacon based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) techniques. To backtrack signals from encrypted beacons without knowing the encryption codes, the cross ambiguity function (CAF) is proposed to estimate the TDOA and FDOA, in which high sampling rates are needed to increase the positioning accuracy of existing CAF algorithms, which entail high calculation loads. To overcome this, an interpolation method is proposed for peaks of Fourier transform capable of maintaining high accuracy at low calculation loads after a well-performed initial acquisition. The numerical simulation shows an improved estimation accuracy of the proposed algorithm compared with existing peak detection methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Introductionmentioning

confidence: 99%

“…The results showed that the TOA and FOA estimation errors of the method can be effectively limited. This algorithm was later applied to Galileo medium‐altitude earth orbiting LUT (MEOLUT) [8].…”

Section: Introductionmentioning

confidence: 99%

Improved position estimation method for locating an encrypted COSPAS‐SARSAT beacon

Lee

Song

Jeong

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…In earlier studies, research of target localization using range and relative velocity measurements is mainly centered on Galileo search and rescue system [21]- [23]. Utilization and investigation of active target detection and tracking, which employ range and relative velocity, have been widely conducted in radar research [24], [25].…”

mentioning

confidence: 99%

“…, 20) T (m) and(28,21) T (m) with a speed 1 m/s, and the position update frequency is 1 Hz. 4 anchors and 3 blockers are deployed in the scenario to imitate a dense NLOS localization environment.…”

mentioning

confidence: 99%

Acoustic Indoor Localization Based on Range and Relative Velocity in Non-Line-of-Sight Environment

Zhang,

Wang,

et al. 2024

IEEE Sensors J.

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This paper investigates a problem of acoustic indoor localization in dense Non-Line-of-Sight (NLOS) environments by using range and relative velocity measurements obtained from received acoustic signals, without the need of additional sensors. The relationship between adjacent positions to be estimated is established by introducing velocity measurements and assuming a first-order motion model. This reduces the number of required Line-of-Sight (LOS) anchors. The principle of the range and relative velocity-based localization method is systematically studied and a basic solution based on a Least Square estimator (LSE) and its closed-form has been proposed. In order to achieve precise localization in dense NLOS environments, a multi-position joint estimation (MPJE) method is proposed. This method involves jointly estimating multiple positions in a short-time position sequence and is solved using a Levenberg-Marquardt (LM) algorithm. A LOS measurements redundancy metric is proposed to balance the localization accuracy and algorithm time cost by adjusting the sequence length. The results obtained from numerical simulations and experimental investigations demonstrate that the proposed range and relative velocity-based localization method outperforms the conventional range-based methods. The number of required LOS anchors for accurate localization is reduced from 3 to 2 for 2D positioning. Additionally, the proposed multi-position joint estimation method requires only 2 LOS anchors and permits the use of only 1 LOS anchor for a short period. This enables its application in dense NLOS environments with a low anchor deployment density, thereby increasing its scene adaptive capability and promotional value.

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Performance of TOA and FOA-based localization for Cospas-Sarsat search and rescue signals

Nicolau

Coulon

Gregoire

et al. 2013

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Research on FOA and TOA Estimation Algorithm of Galileo SAR Signal

Cited by 3 publications

References 2 publications

Improved position estimation method for locating an encrypted COSPAS‐SARSAT beacon

Improved position estimation method for locating an encrypted COSPAS‐SARSAT beacon

Acoustic Indoor Localization Based on Range and Relative Velocity in Non-Line-of-Sight Environment

Performance of TOA and FOA-based localization for Cospas-Sarsat search and rescue signals

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