A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers

Deng, Zhongliang; Mo, Jun; Jia, Buyun; Bian, Xinmei

doi:10.3390/electronics7120376

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…However, rough Doppler frequency estimates cause the tracking loop to spend a long time on convergence, which cannot satisfy the requirements of a fast convergence rate and short time to first fix (TTFF) [3]. To improve Doppler frequency estimation accuracy, many algorithms are proposed to address this problem [4][5][6][7][8][9][10][11][12][13][14][15][16]. These algorithms are generally divided into two categories: the one-step algorithm and the two-step algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A novel fine Doppler frequency acquisition algorithm for BDS‐3 B1C signal based on an adaptive filter

Qiu

Wang

et al. 2021

IET Radar Sonar & Navi

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Fine Doppler frequency estimation has an important role in accelerating the convergence of the tracking loop in a global navigation satellite system (GNSS) receiver to achieve short time to first fix. BDS-3 started broadcasting a civil B1C signal to provide open services for global users, which is beneficial for GNSS-based applications. Therefore, a fine Doppler frequency acquisition algorithm based on an adaptive filter is proposed, whose purpose is to acquire the BDS-3 B1C signal Doppler frequency accurately after the completion of coarse acquisition. The proposed algorithm is based on a first-order complex-coefficients adaptive filter. The adaptive filter depends on the proposed adaptation algorithm to track the input BDS-3 B1C signal. An accurate Doppler frequency estimate is extracted. Simulation results show the proposed algorithm has high acquisition sensitivity, high acquisition accuracy, short acquisition time, and few hardware resources consumption, and also works well under many different coarse acquisition strategies. Overall, the proposed algorithm is better than the generic second-order frequency locked loop. Consequently, the proposed algorithm has high practical value.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

“…Although the FLL consumes less hardware resources, it takes a long time to converge. The last is based on the Gram-Schmidt orthogonal method [11,12], which, however, needs a lot of iterations and has a heavy computational burden.…”

Section: Introductionmentioning

confidence: 99%

A novel fine Doppler frequency acquisition algorithm for BDS‐3 B1C signal based on an adaptive filter

Qiu

Wang

et al. 2021

IET Radar Sonar & Navi

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“…In [ 46 , 47 ], two machine learning methods were proposed to achieve the nonlinear initial alignment of SINS under the condition of large misalignment angles, of which one was based on Gaussian process regression (GPR) [ 46 ], the other utilized a combination of Gaussian mixture model (GMM), expectation–maximization (EM), and UKF filter [ 47 ]. In order to reduce the effects of nonlinear errors, the nonlinear error modeling technique based fast orthogonal search (FOS) was introduced, which have been applied to the radar (RAD)/reduced inertial sensor system (RISS) integration [ 48 ], fine frequency estimation of time and code division-orthogonal frequency division multiplexing (TC-OFDM) receivers [ 49 ], INS/global navigation satellite system (GNSS) integrated navigation systems [ 50 ] and MEMS inertial sensors in mobile devices [ 51 ]. However, almost all of these nonlinear alignment methods mentioned above have some shortcomings such as complex algorithm, heavy computational load, difficulties in parameter optimization, insufficient stability, and poor accuracy, etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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“…Then the estimated parameters are passed to the signal tracking process to realize continuous fine parameter estimation. The acquisition accuracy directly influences the tracking performance such as the convergence time of tracking loop [24]. Hence, improving the acquisition accuracy in a weak signal environment is essential in CNFS.…”

Section: Introductionmentioning

confidence: 99%

Fine Frequency Acquisition Scheme in Weak Signal Environment for a Communication and Navigation Fusion System

et al. 2019

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A novel communication and navigation fusion system (CNFS) was developed to realized high accuracy positioning in constrained conditions. Communication and navigation fusion signal transmitted by base stations are in the same time and frequency band but are allocated different power levels. The positioning receiver of CNFS requires signal coverage of at least four base stations to realize positioning. The improvement of receiver sensitivity is an important way to expand signal coverage of base station. As an essential stage of signal processing in CNFS positioning receiver, signal acquisition requires a trade-off between improvement of acquisition frequency accuracy and reduction in computational load. A new acquisition algorithm called PMF-FC-BA-FFT method is proposed to acquire the carrier frequency accurately with lower computational load in a weak signal environment. The received signal is firstly filtered by partially matched filters (PMF) with local replica pseudorandom noise (PRN) sequences being coefficients to strip off the PRN code in the signal. Frequency compensation (FC) was performed to avoid the large attenuation in block accumulation (BA) and generate a series of signals with a small frequency offset step. Block accumulation was then executed. Finally, the acquisition detection was performed based on a series of fast Fourier transformation (FFT) outputs to obtain acquisition results with fine frequency estimation. Simulations and experimental tests results show that the proposed method can realize high accuracy frequency acquisition in a weak signal environment with fewer computational resources compared with existing acquisition methods.

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A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers

Cited by 6 publications

References 19 publications

A novel fine Doppler frequency acquisition algorithm for BDS‐3 B1C signal based on an adaptive filter

A novel fine Doppler frequency acquisition algorithm for BDS‐3 B1C signal based on an adaptive filter

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Fine Frequency Acquisition Scheme in Weak Signal Environment for a Communication and Navigation Fusion System

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