scholarly journals Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 420
Author(s):  
Iñigo Cortés ◽  
Johannes Rossouw van der Merwe ◽  
Elena Simona Lohan ◽  
Jari Nurmi ◽  
Wolfgang Felber
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
System Performance ◽  
Minimum Mean Square Error ◽  
Lookup Table ◽  
Time Varying ◽  
Process Noise ◽  
Adaptive Tracking ◽  
State Process ◽  
Loop Bandwidth

This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements’ distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA’s loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (C/N0)-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver’s tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity.

Steady-state analysis of DC converter using Galerkin’s method

2019 ◽  
Vol 38 (6) ◽  
pp. 2057-2069
Author(s):  
Igor Korotyeyev
Keyword(s):  
Steady State ◽  
Differential Equations ◽  
Galerkin Method ◽  
State Space ◽  
Time Interval ◽  
Time Varying ◽  
Content Type ◽  
State Process ◽  
The Galerkin Method ◽  
Steady State Process

Purpose The purpose of this paper is to present the Galerkin method for analysis of steady-state processes in periodically time-varying circuits. Design/methodology/approach A converter circuit working on a time-varying load is often controlled by different signals. In the case of incommensurable frequencies, one can find a steady-state process only via calculation of a transient process. As the obtained results will not be periodical, one must repeat this procedure to calculate the steady-state process on a different time interval. The proposed methodology is based on the expansion of ordinary differential equations with one time variable into a domain of two independent variables of time. In this case, the steady-state process will be periodical. This process is calculated by the use of the Galerkin method with bases and weight functions in the form of the double Fourier series. Findings Expansion of differential equations and use of the Galerkin method enable discovery of the steady-state processes in converter circuits. Steady-state processes in the circuits of buck and boost converters are calculated and results are compared with numerical and generalized state-space averaging methods. Originality/value The Galerkin method is used to find a steady-state process in a converter circuit with a time-varying load. Processes in such a load depend on two incommensurable signals. The state-space averaging method is generalized for extended differential equations. A balance of active power for extended equations is shown.

scholarly journals Evaluation of Adaptive Loop-Bandwidth Tracking Techniques in GNSS Receivers

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 502
Author(s):  
Iñigo Cortés ◽  
Johannes Rossouw van der Merwe ◽  
Jari Nurmi ◽  
Alexander Rügamer ◽  
Wolfgang Felber
Keyword(s):  
System Performance ◽  
Time Complexity ◽  
Satellite System ◽  
Optimal Performance ◽  
Tracking Performance ◽  
System Perspective ◽  
Adaptive Tracking ◽  
Loop Bandwidth ◽  
Tracking Loops ◽  
Gnss Receivers

Global navigation satellite system (GNSS) receivers use tracking loops to lock onto GNSS signals. Fixed loop settings limit the tracking performance against noise, receiver dynamics, and the current scenario. Adaptive tracking loops adjust these settings to achieve optimal performance for a given scenario. This paper evaluates the performance and complexity of state-of-the-art adaptive scalar tracking techniques used in modern digital GNSS receivers. Ideally, a tracking channel should be adjusted to both noisy and dynamic environments for optimal performance, defined by tracking precision and loop robustness. The difference between the average tracking jitter of the discriminator’s output and the square-root Cramér-Rao bound (CRB) indicates the loops’ tracking capability. The ability to maintain lock characterizes the robustness in highly dynamic scenarios. From a system perspective, the average lock indicator is chosen as a metric to measure the performance in terms of precision, whereas the average number of visible satellites being tracked indicates the system’s robustness against dynamics. The average of these metrics’ product at different noise levels leads to a reliable system performance metric. Adaptive tracking techniques, such as the fast adaptive bandwidth (FAB), the fuzzy logic (FL), and the loop-bandwidth control algorithm (LBCA), facilitate a trade-off for optimal performance. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. All three methods steer a third-order adaptive phase locked loop (PLL) and are tested in simulated scenarios emulating static and high-dynamic vehicular conditions. The measured tracking performance, system performance, and time complexity of each algorithm present a detailed analysis of the adaptive techniques. The results show that the LBCA with a piece-wise linear approximation is above the other adaptive loop-bandwidth tracking techniques while preserving the best performance and lowest time complexity. This technique achieves superior static and dynamic system performance being 1.5 times more complex than the traditional tracking loop.

Asynchronous Multi-Sensor Fusion Algorithm Based on the Steady-State Kalman Filter

2014 ◽  
Vol 490-491 ◽  
pp. 781-788
Author(s):  
Hui Ma ◽  
Xian Fei Liu
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Sensor Fusion ◽  
Estimation Error ◽  
Minimum Mean Square Error ◽  
Fusion Center ◽  
Fusion Algorithm ◽  
Error Covariance ◽  
Optimal Information ◽  
Optimal Fusion

The paper studies an asynchronous multi-sensor fusion problem based a kind of asynchronous multi-sensor dynamic system. Firstly, this paper presents a centralized fusion algorithm based on the Kalman filter without ignoring the correlation between process noise and augmented measurement noise. It is optimal in minimum mean square error. Then using the steady-state Kalman filter to estimate and fuse. Secondly, in the condition that the local sensor estimation error is associated, a distributed fusion algorithm is given by utilizing S.L. Sun optimal information fusion criterion in minimum error covariance matrix trace at fusion center. In distributed algorithm, the value transmitting to the fusion center is determined by the local sensor estimation based on the steady-state Kalman filter and one step predictive value. Since both optimal fusion algorithm standards are different, so the fusion precision will vary. Finally the effectiveness of the algorithm is verified by computer simulation.

Robust Kalman-Type Filter for Non-Gaussian Noise: Performance Analysis With Unknown Noise Covariances

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Seyed Fakoorian ◽  
Alireza Mohammadi ◽  
Vahid Azimi ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Optimality Criterion ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Process Noise ◽  
Noise Covariance ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Mean Square Errors

The Kalman filter (KF) is optimal with respect to minimum mean square error (MMSE) if the process noise and measurement noise are Gaussian. However, the KF is suboptimal in the presence of non-Gaussian noise. The maximum correntropy criterion Kalman filter (MCC-KF) is a Kalman-type filter that uses the correntropy measure as its optimality criterion instead of MMSE. In this paper, we modify the correntropy gain in the MCC-KF to obtain a new filter that we call the measurement-specific correntropy filter (MSCF). The MSCF uses a matrix gain rather than a scalar gain to provide better selectivity in the way that it handles the innovation vector. We analytically compare the performance of the KF with that of the MSCF when either the measurement or process noise covariance is unknown. For each of these situations, we analyze two mean square errors (MSEs): the filter-calculated MSE (FMSE) and the true MSE (TMSE). We show that the FMSE of the KF is less than that of the MSCF. However, the TMSE of the KF is greater than that of the MSCF under certain conditions. Illustrative examples are provided to verify the analytical results.

Spectroscopic Studies of Asparaginyl-tRNA Synthetase from Entamoeba histolytica

2019 ◽  
Vol 26 (6) ◽  
pp. 435-448
Author(s):  
Priyanka Biswas ◽  
Dillip K. Sahu ◽  
Kalyanasis Sahu ◽  
Rajat Banerjee
Keyword(s):  
Circular Dichroism ◽  
Steady State ◽  
Entamoeba Histolytica ◽  
Protein Concentration ◽  
Trna Synthetase ◽  
Solvent Exposure ◽  
Steady State Fluorescence ◽  
State Process ◽  
Wide Range ◽  
Circular Dichroïsm

Background: Aminoacyl-tRNA synthetases play an important role in catalyzing the first step in protein synthesis by attaching the appropriate amino acid to its cognate tRNA which then transported to the growing polypeptide chain. Asparaginyl-tRNA Synthetase (AsnRS) from Brugia malayi, Leishmania major, Thermus thermophilus, Trypanosoma brucei have been shown to play an important role in survival and pathogenesis. Entamoeba histolytica (Ehis) is an anaerobic eukaryotic pathogen that infects the large intestines of humans. It is a major cause of dysentery and has the potential to cause life-threatening abscesses in the liver and other organs making it the second leading cause of parasitic death after malaria. Ehis-AsnRS has not been studied in detail, except the crystal structure determined at 3 Å resolution showing that it is primarily α-helical and dimeric. It is a homodimer, with each 52 kDa monomer consisting of 451 amino acids. It has a relatively short N-terminal as compared to its human and yeast counterparts. Objective: Our study focusses to understand certain structural characteristics of Ehis-AsnRS using biophysical tools to decipher the thermodynamics of unfolding and its binding properties. Methods: Ehis-AsnRS was cloned and expressed in E. coli BL21DE3 cells. Protein purification was performed using Ni-NTA affinity chromatography, following which the protein was used for biophysical studies. Various techniques such as steady-state fluorescence, quenching, circular dichroism, differential scanning fluorimetry, isothermal calorimetry and fluorescence lifetime studies were employed for the conformational characterization of Ehis-AsnRS. Protein concentration for far-UV and near-UV circular dichroism experiments was 8 µM and 20 µM respectively, while 4 µM protein was used for the rest of the experiments. Results: The present study revealed that Ehis-AsnRS undergoes unfolding when subjected to increasing concentration of GdnHCl and the process is reversible. With increasing temperature, it retains its structural compactness up to 45ºC before it unfolds. Steady-state fluorescence, circular dichroism and hydrophobic dye binding experiments cumulatively suggest that Ehis-AsnRS undergoes a two-state transition during unfolding. Shifting of the transition mid-point with increasing protein concentration further illustrate that dissociation and unfolding processes are coupled indicating the absence of any detectable folded monomer. Conclusion: This article indicates that GdnHCl induced denaturation of Ehis-AsnRS is a two – state process and does not involve any intermediate; unfolding occurs directly from native dimer to unfolded monomer. The solvent exposure of the tryptophan residues is biphasic, indicating selective quenching. Ehis-AsnRS also exhibits a structural as well as functional stability over a wide range of pH.

scholarly journals Time-varying cointegration and the Kalman filter

2021 ◽  
pp. 1-21
Author(s):  
Burak Alparslan Ero˜glu ◽  
J. Isaac Miller ◽  
Taner Yi˜git
Keyword(s):  
Kalman Filter ◽  
Time Varying
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