An Approach to Improve the Positioning Performance of GPS/INS/UWB Integrated System with Two-Step Filter

Li, Zengke; Ren, Wei; Gao, Jingxiang; Wang, Jian

doi:10.3390/rs10010019

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…This source of data may provoke cumulative errors that must be removed [3]. Relative positioningbased techniques include inertial navigation systems (INSs) [4], light detection and ranging (LiDAR) [5], visual simultaneous localization and mapping (SLAM) using cameras [6], and pedestrian dead reckoning (PDR) [7]. Different approaches attempt to solve the problem of cumulative error in relative systems using additional sensors based on primary technologies, as radio frequency (RF), optics, acoustics, etc.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Villadangos

Ureña

Domínguez

et al. 2021

Sensors

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Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor β (GCC-PHAT-β). To improve the performance of the GCC-PHAT-β in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

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Section: Introductionmentioning

confidence: 99%

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Villadangos

Ureña

Domínguez

et al. 2021

Sensors

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“…Studies have shown that the navigation strategies which integrate one or more navigation means, such as GPS [5][6][7], Spectral Redshift [8], Polarized Light [9] etc., with INS can obtain complementary advantages and achieve better navigation results. The Unmanned Aerial Vehicle (UAV) has played an increasingly important role in various fields in recent years [10], to achieve better navigation results, GPS/INS is the most widely used integrated navigation system especially for UAVs [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

et al. 2019

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Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.

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“…The goal of hyperspectral unmixing is to extract the pure spectral signatures (called endmembers) in the scene [6] at first and then estimate the corresponding proportions (called abundances) of these endmembers [7]. There are two main models of hyperspectral unmixing: linear mixture model (LMM) [8,9] and nonlinear mixture model (NLMM) [10][11][12]. Different from the NLMM, the LMM has the advantages of simplicity, high efficiency, and clear physical meaning.…”

Section: Introductionmentioning

confidence: 99%

Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

Zheng

Shim

et al. 2019

Remote Sensing

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Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.

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An Approach to Improve the Positioning Performance of GPS/INS/UWB Integrated System with Two-Step Filter

Cited by 31 publications

References 16 publications

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

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