Adaptive estimation of the dispersion noise matrix at linear measurements based on non-periodic accurate observations

2020 ◽  
Keyword(s):  
Kalman Filter ◽  
Adaptive Estimation ◽  
Traditional Approach ◽  
Reference Points ◽  
Dispersion Matrix ◽  
Preliminary Calculation ◽  
Linear Measurements ◽  
Measuring Systems ◽  
Object Based ◽  
The Stability

The lack of modern methods for ensuring the stability of Kalman filtering under the priori uncertainty condition of the dispersion matrix measurement interference is the absence of strict criteria for choosing adaptation coefficients when calculating the posterior covariance matrix or the inability to adaptively evaluate in real time from the minimum covariance of the update sequence due to the need for its preliminary calculation. The article is devoted to the development of a new approach to adapting the Kalman filter, using the ability to obtain accurate measurements for a wide class of objects that irregularly enter their monitoring system (reference point, reference points, radio frequency identifiers, etc.). It is shown that for the distinguished case, the problem of adaptive estimation of the dispersion noise matrix of a linear meter in the Kalman filter can be solved analytically by using matrix methods of linear algebra. A numerical example illustrating the effectiveness of the procedure for estimating the state vector of a moving object based on the proposed algorithm in comparison with the traditional approach is presented. The simplicity and accuracy of the proposed algorithm provide the possibility of its effective application for the widest class of information-measuring systems. Keywords adaptive estimation; dispersion matrix of measurement noise; Kalman filter; non-periodic accurate observations

Adaptive estimation of UVs navigation parameters by irregular inertial-satellite measurements

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sergey V. Sokolov ◽  
Arthur I. Novikov
Keyword(s):  
Kalman Filter ◽  
Adaptive Estimation ◽  
Unmanned Vehicles ◽  
Dispersion Matrix ◽  
Navigation Systems ◽  
Satellite Measurements ◽  
Preliminary Calculation ◽  
Adaptation Algorithm ◽  
Estimation Errors ◽  
Content Type

PurposeThere are shortcomings of modern methods of ensuring the stability of Kalman filtration in unmanned vehicles’ (UVs) navigation systems under the condition of a priori uncertainty of the dispersion matrix of measurement interference. First, it is the absence of strict criteria for the selection of adaptation coefficients in the calculation of the a posteriori covariance matrix. Secondly, it is the impossibility of adaptive estimation in real time from the condition of minimum covariance of the updating sequence due to the necessity of its preliminary calculation.Design/methodology/approachThis paper considers a new approach to the construction of the Kalman filter adaptation algorithm. The algorithm implements the possibility of obtaining an accurate adaptive estimation of navigation parameters for integrated UVs inertial-satellite navigation systems, using the correction of non-periodic and unstable inertial estimates by high-precision satellite measurements. The problem of adaptive estimation of the noise dispersion matrix of the meter in the Kalman filter can be solved analytically using matrix methods of linear algebra. A numerical example illustrates the effectiveness of the procedure for estimating the state vector of the UVs’ navigation systems.FindingsAdaptive estimation errors are sharply reduced in comparison with the traditional scheme to the range from 2 to 7 m in latitude and from 1.5 to 4 m in longitude.Originality/valueThe simplicity and accuracy of the proposed algorithm provide the possibility of its effective application to the widest class of UVs’ navigation systems.

scholarly journals Innovation-based fractional order adaptive Kalman filter

2020 ◽  
Vol 71 (1) ◽  
pp. 60-64
Author(s):  
Ravi Pratap Tripathi ◽  
Ashutosh Kumar Singh ◽  
Pavan Gangwar
Keyword(s):  
Kalman Filter ◽  
Fractional Order ◽  
Adaptive Estimation ◽  
Lyapunov Theory ◽  
Measurement Noise ◽  
Position Tracking ◽  
Practical Application ◽  
Adaptive Kalman Filter ◽  
Noise Parameters ◽  
The Stability

AbstractKalman Filter (KF) is the most widely used estimator to estimate and track the states of target. It works well when noise parameters and system models are well defined in advance. Its performance degrades and starts diverging when noise parameters (mainly measurement noise) changes. In the open literature available researchers has used the concept of Fractional Order Kalman Filter (FOKF) to stabilize the KF. However in the practical application there is a variation in the measurement noise, which will leads to divergence and degradation in the FOKF approach. An Innovation Adaptive Estimation (IAE) based FOKF algorithm is presented in this paper. In order to check the stability of the proposed method, Lyapunov theory is used. Position tracking simulation has been performed for performance evaluation, which shows the better result and robustness.

scholarly journals Cats in the forest: predicting habitat adaptations from humerus morphometry in extant and fossil Felidae (Carnivora)

Paleobiology ◽  
10.1666/12001 ◽  
2013 ◽  
Vol 39 (3) ◽  
pp. 323-344 ◽  
Author(s):  
Carlo Meloro ◽  
Sarah Elton ◽  
Julien Louys ◽  
Laura C. Bishop ◽  
Peter Ditchfield
Keyword(s):  
Morphological Variability ◽  
Olduvai Gorge ◽  
Environmental Predictors ◽  
Linear Measurements ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Related Group ◽  
Discriminant Analyses ◽  
The Stability ◽  
The Relationship

Mammalian carnivores are rarely incorporated in paleoenvironmental reconstructions, largely because of their rarity within the fossil record. However, multivariate statistical modeling can be successfully used to quantify specific anatomical features as environmental predictors. Here we explore morphological variability of the humerus in a closely related group of predators (Felidae) to investigate the relationship between morphometric descriptors and habitat categories. We analyze linear measurements of the humerus in three different morphometric combinations (log-transformed, size-free, and ratio), and explore four distinct ways of categorizing habitat adaptations. Open, Mixed, and Closed categories are defined according to criteria based on traditional descriptions of species, distributions, and biome occupancy. Extensive exploratory work is presented using linear discriminant analyses and several fossils are included to provide paleoecological reconstructions.We found no significant differences in the predictive power of distinct morphometric descriptors or habitat criteria, although sample splitting into small and large cat guilds greatly improves the stability of the models. Significant insights emerge for three long-canine cats:Smilodon populator,Paramachairodus orientalis, andDinofelissp. from Olduvai Gorge (East Africa).S. populatorandP. orientalisare both predicted to have been closed-habitat adapted taxa. The false “sabertooth”Dinofelissp. from Olduvai Gorge is predicted to be adapted to mixed habitat. The application of felid humerus ecomorphology to the carnivoran record of Olduvai Gorge shows that the older stratigraphic levels (Bed I, 1.99–1.79 Ma) included a broader range of environments than Beds II or V, where there is an abundance of cats adapted to open environments.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

Dynamic gyroscope sensors fusion and calibration using Kalman filter

2018 ◽  
Vol 7 (2.3) ◽  
pp. 18
Author(s):  
Mishell D. Lawas ◽  
Sherwin A. Guirnaldo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Systems Design ◽  
Mean Square ◽  
Fusion Technique ◽  
System Experiment ◽  
Aerial Vehicle ◽  
Sensors Fusion ◽  
The Stability ◽  
Actual Test

The stability of an Unmanned Aerial Vehicle (UAV) during actual flight conditions is one parameter that is very important in systems design in Avionics. In this research, two sensors, the autopilot microcontroller and the smartphone gyroscope sensing mechanism, are fused together and calibrated to monitor the flying behavior of the UAV prior to actual test flights. The two fused sensors and installed inside the UAV for relatively increased sensing accuracy and best flight monitoring capabilities. A Kalman filter is used as fusion technique and a Stewart Motion tracker is also used to test the ruggedness and accuracy of the fused sensor system. Experiment results show that fused system can give an overall mean square error or 1.9729.

An algorithm for the 3D ROck Slope Kinematic Analysis (ROKA) based on RPAS digital photogrammetry data.

2021 ◽  
Author(s):  
Niccolò Menegoni ◽  
Daniele Giordan ◽  
Cesare Perotti
Keyword(s):  
Kinematic Analysis ◽  
Rock Slope ◽  
Traditional Approach ◽  
Rock Slopes ◽  
Digital Photogrammetry ◽  
Modes Of Failure ◽  
Wedge Sliding ◽  
Flexural Toppling ◽  
The Stability ◽  
Digital Outcrop

<p>Among the several adopted methods for the kinematic analysis of the possible modes of failure that could affect a rock slope, the Markland test is the most used. Whereas, it has the advantage of being simple and fast, it has some limits, as the impossibility to manually consider the several different slope orientations and their interaction with the discontinuity dimensions and positions.</p><p>Recently, the improvements in the Remote Piloted Aerial System (RPAS) digital photogrammetry techniques for the development and mapping of Digital Outcrop Models (DOMs) have given the possibility of developing new automatized digital approaches. In this study, ROKA (ROck slope Kinematic Analysis) algorithm is presented. It is an open-source algorithm, written in MATLAB language, which aims to perform the kinematic analysis of the stability of a rock slope using the discontinuity measurements collected onto 3D DOMs. Its main advantage is the possibility to identify the possible critical combination between the 3D georeferenced discontinuities and the local surface of the slope. In particular, the critical combinations that can activate the planar sliding, flexural toppling, wedge sliding and direct toppling modes of failures can be detected and highlighted directly on the DOM. Hence, the ROKA algorithm can make the traditional approach for the kinematic analysis of a rock slope more effective, allowing not only to simplify the analysis, but also to increase its detail. This can be very important, in particular, for the analysis of large and complex rock slopes.</p>

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

Stability of Vlasov equilibria. Part 1. General theory

1982 ◽  
Vol 27 (1) ◽  
pp. 13-24 ◽  
Author(s):  
K. R. Symon ◽  
C. E. Seyler ◽  
H. R. Lewis
Keyword(s):  
Distribution Function ◽  
General Theory ◽  
Linear Stability ◽  
Vlasov Equation ◽  
Stability Problem ◽  
Dispersion Matrix ◽  
Initial Value ◽  
Spectral Matrix ◽  
Concise Representation ◽  
The Stability

We present a general formulation for treating the linear stability of inhomogeneous plasmas for which at least one species is described by the Vlasov equation. Use of Poisson bracket notation and expansion of the perturbation distribution function in terms of eigenfunctions of the unperturbed Liouville operator leads to a concise representation of the stability problem in terms of a symmetric dispersion functional. A dispersion matrix is derived which characterizes the solutions of the linearized initial-value problem. The dispersion matrix is then expressed in terms of a dynamic spectral matrix which characterizes the properties of the unperturbed orbits, in so far as they are relevant to the linear stability of the system.

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