Performance comparison of Kalman filter and maximum likelihood carrier phase tracking for weak GNSS signals

He, Zhe; Petovello, Mark G.

doi:10.1109/ipin.2015.7346959

Cited by 5 publications

(4 citation statements)

References 13 publications

(14 reference statements)

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“…Therefore, if we use the estimated frequency directly in weak signal environment, the non-negligible estimated error in the carrier loop will occur, and finally restrict the improvement of tracking sensitivity and accuracy under weak signals. Since KF is the optimal filter in linear discrete systems [ 33 ]. KF is adopted in this paper to smooth the estimation of the parameters after a non-linear estimation by MLE.…”

Section: Mle Parameter Estimation Modelmentioning

confidence: 99%

A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals

Liu¹,

Bian²,

Deng³

et al. 2018

Sensors

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Digital broadcasting signals represent a promising positioning signal for indoors applications. A novel positioning technology named Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is mainly discussed in this paper, which is based on China mobile multimedia broadcasting (CMMB). Signal strength is an important factor that affects the carrier loop performance of the TC-OFDM receiver. In the case of weak TC-OFDM signals, the current carrier loop algorithm has large residual carrier errors, which limit the tracking sensitivity of the existing carrier loop in complex indoor environments. This paper proposes a novel carrier loop algorithm based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) to solve the above problem. The discriminator of the current carrier loop is replaced by the MLE discriminator function in the proposed algorithm. The Levenberg-Marquardt (LM) algorithm is utilized to obtain the MLE cost function consisting of signal amplitude, residual carrier frequency and carrier phase, and the MLE discriminator function is derived from the corresponding MLE cost function. The KF is used to smooth the MLE discriminator function results, which takes the carrier phase estimation, the angular frequency estimation and the angular frequency rate as the state vector. Theoretical analysis and simulation results show that the proposed algorithm can improve the tracking sensitivity of the TC-OFDM receiver by taking full advantage of the characteristics of the carrier loop parameters. Compared with the current carrier loop algorithms, the tracking sensitivity is effectively improved by 2–4 dB, and the better performance of the proposed algorithm is verified in the real environment.

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Section: Mle Parameter Estimation Modelmentioning

confidence: 99%

A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals

Liu¹,

Bian²,

Deng³

et al. 2018

Sensors

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“…Petovello investigated extending the integration time for enhancing the weak signal tracking [13,14]. Apart from enhancing the standalone receiver's performance, integrating the GPS with other sensors has been one of the most promising and effective methods, especially the GPS/SINS integration [15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…When the GPS is unavailable attenuated, the SINS can provide short-term accurate navigation solutions instead of the GPS. Moreover, for dynamic applications, the SINS can provide navigation solutions at higher rate, which can fill the gap between GPS information and smooth the navigation solutions [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Filtering Model for GPS/SINS Ultratight Integration System

Wang

Jiang

2020

Mathematical Problems in Engineering

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Global Positioning System (GPS) and strap-down inertial navigation system (SINS) are recognized as highly complementary and widely employed in the community. The GPS has the advantage of providing precise navigation solutions without divergence, but the GPS signals might be blocked and attenuated. The SINS is a totally self-contained navigation system which is hardly disturbed. The GPS/SINS integration system could utilize the advantages of both the GPS and SINS and provide more reliable navigation solutions. According to the data fusion strategies, the GPS/SINS integrated system could be divided into three different modes: loose, tight, and ultratight integration (LI, TI, and UTC). In the loose integration mode, position and velocity difference from the GPS and SINS are employed to compose measurement vector, in which the vector dimension has nothing to do with the amount of the available satellites. However, in the tight and ultratight modes, difference of pseudoranges and pseudorange rates from the GPS and SINS are employed to compose the measurement vector, in which the measurement vector dimension increases with the amount of available satellites. In addition, compared with the loose integration mode, clock bias and drift are included in the integration state model. The two characteristics magnify the computation load of the tight and ultratight modes. In this paper, a new efficient filter model was proposed and evaluated. Two schemes were included in this design for reducing the computation load. Firstly, a difference between pseudorange measurements was determined, by which clock bias and drift were excluded from the integration state model. This step reduced the dimension of the state vector. Secondly, the integration filter was divided into two subfilters: pseudorange subfilter and pseudorange rate subfilter. A federated filter was utilized to estimate the state errors optimally. In the second step, the two subfilters could run in parallel and the measurement vector was divided into two subvectors with lower dimension. A simulation implemented in MATLAB software was conducted to evaluate the performance of the new efficient integration method in UTC. The simulation results showed that the method could reduce the computation load with the navigation solutions almost unchanged.

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“…Instead the signal is processed in batches. In each batch the phase is estimated with the ML principle [6], [7], i.e., searching for the signal parameters which maximize the probability density function for a given batch of received signal samples. Batch processing offers the advantage that the signal is not tracked based on its assumed dynamics and therefore also severe changes in phase and amplitude from one epoch to another can be tracked.…”

Section: Introductionmentioning

confidence: 99%

Dual Kalman filtering based GNSS phase tracking for scintillation mitigation

Fohlmeister

Antreich

Nossek

2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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This paper treats the problem of tracking the global navigation satellite system's (GNSS) signal phase under ionospheric scintillations and to generate an estimate of the scintillation phase and amplitude. In the case of ionospheric scintillations traditional phase locked loop (PLL) based GNSS receivers have problems to track the signal phase accurately. Moreover, these receivers do not estimate the scintillation phase and amplitude, which is evaluated in ionospheric monitoring applications. Therefore, Kalman filter based phase tracking algorithms have been proposed, which track line-of-sight (LOS) phase and scintillation amplitude and phase separately with different state space models. For modeling the scintillation phase and amplitude auto-regressive (AR) models can be used. In this case the parameters for the AR model have to be estimated together with the LOS and scintillation phase and amplitude. We propose to use a second Kalman filter for the estimation of the AR model parameters. This Kalman filter takes the scintillation phase and amplitude estimates from the first Kalman filter as measurement and the AR model parameters as system state. Simulation results for the global positioning system (GPS) show that the proposed algorithm achieves the same estimation performance for scintillation phase and amplitude as an algorithm with a priori knowledge on the AR model parameters.

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Performance comparison of Kalman filter and maximum likelihood carrier phase tracking for weak GNSS signals

Cited by 5 publications

References 13 publications

A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals

A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals

A New Efficient Filtering Model for GPS/SINS Ultratight Integration System

Dual Kalman filtering based GNSS phase tracking for scintillation mitigation

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