Linear Kalman Filter for Dead Time Affected Measurement Signals Implemented in a Small Scale Automated Guided Vehicle

2013 ◽  
pp. 193-200
Author(s):  
F. Lütteke ◽  
J. Franke
Keyword(s):  
Kalman Filter ◽  
Dead Time ◽  
Small Scale ◽  
Automated Guided Vehicle

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

Improved GNSS Localization with the Use of DOP Parameter

2014 ◽  
Vol 611 ◽  
pp. 450-466 ◽  
Author(s):  
František Duchoň ◽  
Jaroslav Hanzel ◽  
Andrej Babinec ◽  
Jozef Rodina ◽  
Peter Paszto ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robots ◽  
Reference Frame ◽  
Small Scale ◽  
Localization Error ◽  
Localization Accuracy ◽  
Gnss Receiver ◽  
Gps Localization

This paper presents the approach to improve localization based on GNSS. The principles of the GPS localization and impact of the DOP parameter on localization error are mathematically analyzed. The algorithm based on the use of DOP parameter and Kalman filter for the improvement of the localization accuracy suitable for small scale outdoor mobile robots and other outdoor applications is proposed. The applicability of the proposed methodology was verified by performed experiments with two common cheap miniature GPS modules and accurate high-end GNSS receiver used as a reference frame for the measurements. The obtained results affirmed the improvement of the localization accuracy.

scholarly journals A survey in the design and control of automated guided vehicle systems

2019 ◽  
Vol 12 (2) ◽  
pp. 41-49
Author(s):  
A. P. Vancea ◽  
I. Orha
Keyword(s):  
Production System ◽  
Production Efficiency ◽  
Ground Level ◽  
Small Scale ◽  
Automated Guided Vehicle ◽  
Total Production ◽  
Automatic Guided Vehicles ◽  
Small Scale Industry ◽  
Vehicle Systems ◽  
And Control

Abstract Automatic guided vehicles (AGVs) play an important role in the small-scale industry as well as the largescale industry in handling materials inside factories from one place to another. In the last days, the materials to be handled are more numerous and as production and demand increase, it strongly influences the transport of materials in desperate need of a vehicle to distribute, position the materials within the industry. AGVs are generally installed with wires at ground level and signals are transmitted through them to be controlled. Due to the emergence of the AGV, the workload of the human being gradually decreased and the production efficiency increased. Thus, the need for an AGV has become more technologically important in the advanced robotic world. Normally, these systems are integrated into a global production system, where is a need to make direct changes in the design and planning of the floor store to get most of them. But in the rapidly changing production system and the adaptable floor store, the implementation of AGV has become very important and difficult, because it depends on many systems, such as wires, frequency, total production, etc. Therefore, it is necessary to develop an independent AGV, which can operate on its own and make decisions based on changes in the environment.

scholarly journals A robust predictor for dead-time systems based on the Kalman filter

2018 ◽  
Vol 51 (25) ◽  
pp. 24-29
Author(s):  
Bruno M. Lima ◽  
Daniel M. Lima ◽  
Julio E. Normey-Rico
Keyword(s):  
Kalman Filter ◽  
Dead Time ◽  
Time Systems

scholarly journals Enhanced location tracking in sensor fusion-assisted virtual reality micro-manipulation environments

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261933
Author(s):  
John David Prieto Prada ◽  
Jintaek Im ◽  
Hyondong Oh ◽  
Cheol Song
Keyword(s):  
Virtual Reality ◽  
Kalman Filter ◽  
Sensor Fusion ◽  
Human Subjects ◽  
Great Accuracy ◽  
Small Scale ◽  
Location Tracking ◽  
Fusion Algorithm ◽  
Measuring Device ◽  
Positioning Errors

Virtual reality (VR) technology plays a significant role in many biomedical applications. These VR scenarios increase the valuable experience of tasks requiring great accuracy with human subjects. Unfortunately, commercial VR controllers have large positioning errors in a micro-manipulation task. Here, we propose a VR-based framework along with a sensor fusion algorithm to improve the microposition tracking performance of a microsurgical tool. To the best of our knowledge, this is the first application of Kalman filter in a millimeter scale VR environment, by using the position data between the VR controller and an inertial measuring device. This study builds and tests two cases: (1) without sensor fusion tracking and (2) location tracking with active sensor fusion. The static and dynamic experiments demonstrate that the Kalman filter can provide greater precision during micro-manipulation in small scale VR scenarios.

scholarly journals Validation of pure rotational Raman temperature data from the Raman Lidar for Meteorological Observations (RALMO) at Payerne

2021 ◽  
Vol 14 (2) ◽  
pp. 1333-1353
Author(s):  
Giovanni Martucci ◽  
Francisco Navas-Guzmán ◽  
Ludovic Renaud ◽  
Gonzague Romanens ◽  
S. Mahagammulla Gamage ◽  
...  
Keyword(s):  
Dead Time ◽  
Signal To Noise Ratio ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Correction Function ◽  
Small Scale ◽  
Temperature Profiles ◽  
Raman Lidar ◽  
Zero Bias ◽  
Meteorological Observations

Abstract. The Raman Lidar for Meteorological Observations (RALMO) is operated at the MeteoSwiss station of Payerne (Switzerland) and provides, amongst other products, continuous measurements of temperature since 2010. The temperature profiles are retrieved from the pure rotational Raman (PRR) signals detected around the 355 nm Cabannes line. The transmitter and receiver systems of RALMO are described in detail, and the reception and acquisition units of the PRR channels are thoroughly characterized. The FastCom P7888 card used to acquire the PRR signal, the calculation of the dead time and the desaturation procedure are also presented. The temperature profiles retrieved from RALMO PRR data during the period going from July 2017 to the end of December 2018 have been validated against two reference operational radiosounding systems (ORSs) co-located with RALMO, i.e. the Meteolabor SRS-C50 and the Vaisala RS41. The ORSs have also served to perform the calibration of the RALMO temperature during the validation period. The maximum bias (ΔTmax), mean bias (μ) and mean standard deviation (σ) of RALMO temperature Tral with respect to the reference ORS, Tors, are used to characterize the accuracy and precision of Tral along the troposphere. The daytime statistics provide information essentially about the lower troposphere due to lower signal-to-noise ratio. The ΔTmax, μ and σ of the differences ΔT=Tral-Tors are, respectively, 0.28, 0.02±0.1 and 0.62±0.03 K. The nighttime statistics provide information for the entire troposphere and yield ΔTmax=0.29 K, μ=0.05±0.34 K and σ=0.66±0.06 K. The small ΔTmax, μ and σ values obtained for both daytime and nighttime comparisons indicate the high stability of RALMO that has been calibrated only seven times over 18 months. The retrieval method can correct for the largest sources of correlated and uncorrelated errors, e.g. signal noise, dead time of the acquisition system and solar background. Especially the solar radiation (scattered into the field of view from the zenith angle Φ) affects the quality of PRR signals and represents a source of systematic error for the retrieved temperature. An imperfect subtraction of the background from the daytime PRR profiles induces a bias of up to 2 K at all heights. An empirical correction f(Φ) ranging from 0.99 to 1 has therefore been applied to the mean background of the PRR signals to remove the bias. The correction function f(Φ) has been validated against the numerical weather prediction model COSMO (Consortium for Small-scale Modelling), suggesting that f(Φ) does not introduce any additional source of systematic or random error to Tral. A seasonality study has been performed to help with understanding if the overall daytime and nighttime zero bias hides seasonal non-zero biases that cancel out when combined in the full dataset.

Experimental validation of the Kalman-type filters for online and real-time state and input estimation

2015 ◽  
Vol 23 (15) ◽  
pp. 2494-2519 ◽  
Author(s):  
Saeed Eftekhar Azam ◽  
Eleni Chatzi ◽  
Costas Papadimitriou ◽  
Andrew Smyth
Keyword(s):  
Kalman Filter ◽  
Physical Models ◽  
Mode Shapes ◽  
Small Scale ◽  
Simultaneous Estimation ◽  
Subspace Identification ◽  
Entire Body ◽  
Acceleration Time Histories ◽  
Time Histories ◽  
Output Only

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.

A Multiscale Local Gain Form Ensemble Transform Kalman Filter (MLGETKF)

2020 ◽  
Author(s):  
Xuguang Wang ◽  
Hristo G. Chipilski ◽  
Craig H. Bishop ◽  
Elizabeth Satterfield ◽  
Nancy Baker ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lead Time ◽  
Potential Temperature ◽  
Scale Model ◽  
Small Scale ◽  
Space Localization ◽  
Ensemble Mean ◽  
The Mean ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform

AbstractA new multiscale, ensemble-based data assimilation (DA) method, MLGETKF (Multiscale Local Gain Form Ensemble Transform Kalman Filter), is introduced. MLGETKF allows simultaneous update of multiple scales for both the mean and ensemble perturbations through assimilating all observations at once. MLGETKF performs DA in independent local volumes, which lends the algorithm a high degree of computational scalability. The multiscale analysis is enabled through the rapid creation of many pseudo ensemble perturbations via a multiscale ensemble modulation procedure. The Kalman gain that is used to update the raw background ensemble mean and perturbations is based on this modulated ensemble, which intrinsically includes multi-scale model space localization.Experiments with a non-cycled statistical model show that the full background covariance estimated by MLGETKF more accurately resembles the shape of the true covariance than a scale-unaware localization. The mean analysis from the best-performing MLGETKF is statistically significantly more accurate than the best performing scale unaware LGETKF. The accuracy of the MLGETKF analysis is more sensitive to small-scale band localization radius than large-scale band. MLGETKF is further examined in a cycling DA context with a Surface Quasi-Geostrophic model. The root-mean-square potential temperature analysis error of the best performing MLGETKF is 17.2% lower than that of the best-performing LGETKF. MLGETKF reduces analysis errors measured in kinetic energy spectra space by 30-80% relative to LGETKF with the largest improvement at large scales. MLGETKF deterministic and ensemble mean forecasts are more accurate than LGETKF for full and large scales up to 5-6 day lead-time and for small scales up to 3-4 day lead-time, gaining 12-hour ~ 1-day of predictability.

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