C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

Liu, Shiming; Li, Sihai; Zheng, Jiangtao; Fu, Qing; Yuan, Ye

doi:10.3390/s20030739

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…where γ k is the residual sequence. The forgetting factor ζ improves the influence of the residual sequence and enhances its role in the filter, usually taken in 0.9 ≤ ζ ≤ 1 [22]. In Equation 12, R k is fixed at each moment in the iterative process of the traditional strong tracking algorithm, but inaccurate R k affects the accuracy of η k in the changing environment [23].To solve the above problem, the same form q(R k ) = IW(R k ; u k , U k ) is introduced after the Bayesian inference, and the measurement noise covariance R k can be expressed as follows:…”

Section: Strong Tracking Principle With Vb Approximationmentioning

confidence: 99%

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Yang

Wang

et al. 2021

Sensors

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Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

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Section: Strong Tracking Principle With Vb Approximationmentioning

confidence: 99%

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Yang

Wang

et al. 2021

Sensors

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“…If a piece of sample data of innovation is used to approximate the covariance matrix of innovation [10], then there is…”

Section: Adaptive Square Root Cubature Kalman Filter Algorithmmentioning

confidence: 99%

“…In highly dynamic applications, the high degree of nonlinearity caused by high dynamic stress will incur greater errors if the EKF algorithm is used. In this case, nonlinear filtering algorithms such as unscented Kalman filters (UKF), particle filters (PF) [9], strong tracking Kalman filters [10], and cubature Kalman filters (CKF) are needed. Among them, the cubature Kalman filter algorithm is a newly proposed algorithm [11] that is derived from rigorous theory and can be accurate to the third order of Taylor expansion.…”

Section: Introductionmentioning

confidence: 99%

High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter

Zhang

Jiang

2021

Sensors

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Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.

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“…In practice, GNSS receivers measure C / N 0 by means of various C / N 0 estimation algorithms [ 27 , 28 ]. In the GNSS context, the narrow-to-wideband power ratio (NWPR) method is usually used as a standard estimator.…”

Section: Quantitative Analyses Of Disruptive Effects Of Large Freqmentioning

confidence: 99%

A New MIMU/GNSS Ultra-Tightly Coupled Integration Architecture for Mitigating Abrupt Changes of Frequency Tracking Errors

Liu

et al. 2020

Micromachines

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We present a new ultra-tightly coupled (UTC) integration architecture of a micro-electromechanical inertial measurement unit (MIMU) and global navigation satellite system (GNSS) to reduce the performance degradation caused by abrupt changes of frequency tracking errors. A large frequency error will lead to a decrease in the carrier-to-noise ratio (C/N0) estimate and an increase in the code discriminator estimation error. The disruptive effects of frequency errors on the estimation of C/N0 and on the code discriminator are quantitatively evaluated via theoretical analyses and Monte Carlo simulations. The new MIMU/GNSS UTC architecture introduces a large frequency error detector and a refined frequency processor based on a retuned frequency in each tracking channel. In addition, an adaptive channel prefilter with multiple fading factors is introduced as an alternate to the conventional prefilter. Numerical simulations based on a highly dynamic trajectory are used to assess performance. The simulation results show that when there is an abrupt step change in the frequency tracking error, the new UTC architecture can effectively suppress the divergence of navigation solutions and the loss of tracking lock, and can significantly reduce the deviation of the C/N0 estimation.

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C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

Cited by 6 publications

References 10 publications

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter

A New MIMU/GNSS Ultra-Tightly Coupled Integration Architecture for Mitigating Abrupt Changes of Frequency Tracking Errors

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