Clock state estimation with the Kalman-like UFIR algorithm via TIE measurement

Contreras-Gonzalez, Jose; Ibarra-Manzano, Oscar; Shmaliy, Yuriy S.

doi:10.1016/j.measurement.2012.08.003

Cited by 11 publications

(10 citation statements)

References 19 publications

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“…Conventionally, we consider all estimates beginning with the first one provided by the UFIR filter on a horizon of N opt = 3500 points. The results confirm a conclusion that was earlier made in [10] and [40]: the UFIR filter is more robust than KF against the GPS time uncertainties and more accurate in clock state estimating. In fact, errors in all of the UFIR estimates (Fig.…”

Section: B Gps-based State Estimation Of Ocxo-based Clocksupporting

confidence: 88%

“…3) are more smoothed than those by KF and lie closer to zero. Note that errors in the KF can be reduced by decreasing the σ 2 y (τ ) values [40] at τ = 1, 10, 100 s. It, however, turns the Allan variance beyond the OCXO specification. The KF thus does not suit the clock model well and the UFIR definitely is a better choice.…”

Section: B Gps-based State Estimation Of Ocxo-based Clockmentioning

confidence: 97%

See 1 more Smart Citation

General Unbiased FIR Filter With Applications to GPS-Based Steering of Oscillator Frequency

Shmaliy

Khan

Zhao

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-The general unbiased finite-impulse response (UFIR) filter proposed in this brief has important structural advantages against its basic predecessor. It can be applied to systems with or without the control input. We derive this filter in a batch form and then design its fast iterative Kalmanlike algorithm using recursions. The iterative UFIR algorithm proposed is applied to the three-state polynomial model which is basic in clock synchronization. We test it by the global positioning system-based frequency steering of an oven-controlled crystal oscillator. Better robustness and higher accuracy of the UFIR filter against the Kalman filter are shown experimentally. PermanentIndex Terms-Control system, global positioning system (GPS)-based frequency steering, Kalman filter (KF), state space, unbiased finite-impulse response (UFIR) filter, unbiasedness.

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Section: B Gps-based State Estimation Of Ocxo-based Clocksupporting

confidence: 88%

Section: B Gps-based State Estimation Of Ocxo-based Clockmentioning

confidence: 97%

General Unbiased FIR Filter With Applications to GPS-Based Steering of Oscillator Frequency

Shmaliy

Khan

Zhao

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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“…In [14], the authors have presented an algorithm based on the KF to address an issue with missing data. Let us notice that the KF optimality has important requirements such as a complete knowledge of the noise statistics, the noise distribution must be strictly Gaussian, an adequate model must be used, and a knowledge of the initial conditions is mandatory [15][16][17][18]. If these requirements are not met, the performance of the KF may drastically degrade and become unacceptable for real world WSN applications [19].…”

Section: Introductionmentioning

confidence: 99%

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

2019

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Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.

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“…That is, it is a finite impulse response (FIR). Obviously this finite impulse response cannot be strictly described by the commonly used first-order autoregressive model because the latter is an infinite impulse response [26][27][28]. On the other hand, at the statistical level, the real-time determination of DID is actually a recursive least squares estimate.…”

Section: Introductionmentioning

confidence: 99%

A Solution for Real-Time Ionospheric Delay Using an Adaptive Kalman Filter Based on Estimating the Variance Component

Yang

Wang

Chang

2018

Mathematical Problems in Engineering

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Real-time solution of Global Navigation Satellite System (GNSS) epoch-differenced ionospheric delay (DID) is of great significance for real-time cycle slip detection and repair of multi-GNSS dual-frequency or trifrequency undifferenced measurements under high ionospheric activity. We construct a dynamic model of DID and perform a real-time estimate of the noise level of DID based on estimating the variance component. The estimated and predicted values of DID are obtained by designing a new adaptive Kalman filter algorithm with colored noise. Combining the predicted value and the detection method for cycle slips for Melbourne-Wübbena (MW) and Geometry-Free (GF) combination and taking into account the correlation between the predicted value and the carrier signal, we estimate the cycle slip, N2, on the second frequency of the carrier signal. The prediction and estimate of DID and detection and repair of dual-frequency cycle slip of multisystem undifferenced phase observations are measured with the GNSS multisystem observational data at different sampling rates (30 s, 15 s, 10 s, and 1 s). The results show that the DID model constructed in this paper is correct. The predicted value of DID has a high accuracy, which can effectively assist in dual-frequency cycle slip detection and repair. (1) The obtained predicted values, the estimated value, and the difference value between the two values of DID are less than 1.2 cm (STD), 1.2 cm (STD), and 0.6 cm (STD), respectively; (2) the precisions of the detection of cycle slip for MW, GF, and N2 are less than 0.083 cycles (STD), 0.4 cm (STD), and 0.071 cycles (STD), respectively; (3) with the obtained predicted value of DID to aid the detection and repair of cycle slip in GNSS double-frequency signals, a success rate of 100% can be reached.

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Clock state estimation with the Kalman-like UFIR algorithm via TIE measurement

Cited by 11 publications

References 19 publications

General Unbiased FIR Filter With Applications to GPS-Based Steering of Oscillator Frequency

General Unbiased FIR Filter With Applications to GPS-Based Steering of Oscillator Frequency

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

A Solution for Real-Time Ionospheric Delay Using an Adaptive Kalman Filter Based on Estimating the Variance Component

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