Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

Farhad, M. A.; Mosavi, Mohammad Reza; Abedi, Ali; Mohammadi, Karim

doi:10.1017/s0373463320000132

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…In contrast to the state-of-the-art, which typically employs more conventional methods for localizing and land vehicle tracking, for instance, the Kalman filter, fuzzy logic is considered a commonly known artificial intelligence (AI) approach. Researchers in [16] created a strong Kalman filter utilizing vector tracking and integrated it with fuzzy logic to change filter parameters to follow weak signals in global navigation satellite system (GNSS) receivers. Thus, the results were superior to the standard procedure.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Dadios,

Jahara Baun,

Louie Enriquez

et al. 2023

Advances in Fuzzy Logic Systems

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This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correct the latitude and longitude of Global Positioning System (GPS) used in locating towed vehicle system for underground imaging. The input used was the collected data from a developed Real-time Kinematic Global Positioning System sensor integrated with Inertial Measurement Unit. Different ANFIS models were developed and evaluated. For latitude correction, ANFIS model with hybrid optimization trained at 300 epochs was chosen, whereas for longitude correction, ANFIS model with hybrid optimization trained at 100 epochs was selected. Both models achieved the lowest Mean Squared Error (MSE), the highest Coefficient of Determination (R2), and lowest Mean Absolute Error (MAE). Moreover, selected best ANFIS models were compared to Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) models, but the results showed that the ANFIS models have superior performances. The selected ANFIS models were verified by testing on the collected actual dataset and the visualized map demonstrated that the corrected GPS latitude and longitude have significantly reduced error, indicating that the fuzzy system with neural network capabilities is a cost-effective and convenient method for error reduction in vehicle localization making it applicable to be integrated for capacitive resistivity underground imaging systems.

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Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Dadios,

Jahara Baun,

Louie Enriquez

et al. 2023

Advances in Fuzzy Logic Systems

View full text Add to dashboard Cite

show abstract

“…e performance of the acquisition algorithm directly affects performance metrics such as the receiver acquisition speed and accuracy [1,2]. For a particular GPS satellite signal, signal acquisition is the estimation of the carrier frequency and code phase of the satellite signal [3][4][5]. Previous research has proposed to use a parallel search algorithm for Doppler frequency and code phase on a two-dimensional grid [6]; however, as finer grid divisions are required to improve acquisition accuracy, while coarser grid divisions are required to improve acquisition speed, this method cannot simultaneously optimize both the acquisition speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Study on the High Accuracy and Fast Acquisition of Satellite Signals Based on the Blind Source Separation Technique

Xie

Bai

et al. 2022

Scientific Programming

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To address the problems of slow acquisition speed and low accuracy faced by existing grid search-based satellite acquisition methods in complex scenarios, this study proposes a high accuracy and fast satellite signal acquisition method based on blind source separation. The proposed method first adopts wavelet threshold denoising to reduce the noise in the overlapped satellite signal received by the receiver so as to improve the signal-to-noise ratio of the satellite signal. On this basis, a subspace estimation method is introduced to construct a blind acquisition algorithm for satellite signals, which achieves high accuracy and fast solution for the Doppler frequency shift and code phase shift of satellite carrier signals and is able to recover the unobservable individual source signals. The effectiveness of the proposed method is verified through experiments and compared against the traditional grid search type acquisition method, which confirmed that the method is suitable for the acquisition requirements of weak signals and has a good engineering application value.

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GNSS/INS integrated navigation algorithm framework based on cascade vector tracking algorithm for USV

Liu,

Wu,

et al. 2024

Marine Geodesy

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Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

Cited by 3 publications

References 30 publications

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

Study on the High Accuracy and Fast Acquisition of Satellite Signals Based on the Blind Source Separation Technique

GNSS/INS integrated navigation algorithm framework based on cascade vector tracking algorithm for USV

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