A Study about Kalman Filters Applied to Embedded Sensors

Valade, Aurélien; Acco, Pascal; Grabolosa, Pierre; Fourniols, Jean-Yves

doi:10.3390/s17122810

Cited by 73 publications

(37 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The condition for updating the value of H 11 is the same as the previous one and it is given by Equation (15).…”

Section: Angle-only Tracking In 2d Radarmentioning

confidence: 99%

“…The described methods make use of existing linearities in non-linear system's models, conditional linearities, special forms of system's matrices, e.g., their block diagonalities, alternative filter's formulations, etc. A problem of dealing with real-time constraints in EKF and UKF implementations on low processing power platforms, like microcontrollers, is discussed in Reference [15]. To save the computational cost, the authors suggest rewriting the filter's equations as a couple of simpler scalar equations and exploiting the sparsity of matrices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

Kaniewski

2020

Sensors

View full text Add to dashboard Cite

The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters of motion, based on measurements, which are non-linearly related to the object’s position. This non-linearity forces designers to use non-linear filters, such as the Extended Kalman Filter mentioned, where linearization of the system’s model is performed in every run of the filter’s loop. The linearization, consisting of calculating Jacobian matrices for non-linear functions in the dynamics and/or observation models, significantly increases the number of operations in comparison to the linear Kalman filter. The method proposed in this paper consists of analyzing a variability of Jacobians and performing the model linearization only when expected changes of those Jacobians exceed a preset threshold. With a properly chosen threshold value, the proposed filter modification leads to a significant reduction of its computational burden and does not noticeably increase its estimation errors. The paper describes a practical simulation-based method of determining the threshold. The accuracy of the filter for various threshold values was tested for simplified models of radar systems.

show abstract

“…The condition for updating the value of H 11 is the same as the previous one and it is given by Equation (15).…”

Section: Angle-only Tracking In 2d Radarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

Kaniewski

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…However, the capacity of the Kalman filter to position nonlinear integrated systems accurately is limited [42,43]. However, for example, there are also some advantages of using this approach [44]. Eom et al established a method for the improvement of physical estimates using multiphysical models and Kalman data fusion filters by processing raw measurements within a sensor [45].…”

Section: Kalman Filtermentioning

confidence: 99%

Seismic Model Parameter Optimization for Building Structures

Lengyel

Stan

Miclea

2020

Sensors

View full text Add to dashboard Cite

Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a key component of seismic design and evaluation. It began more than 100 years ago when seismic regulations adopted static analyzes with lateral loads of about 10% of the weight of the structure. Due to the dynamics and non-linear response of the structures, advanced analytical procedures were implemented over time. The authors’ approach is the following: having a nonlinear dynamic model (in this case, a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints) and at least two datasets of a building, the parameters of the building’s model are estimated using optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE). Not having much expertise on structural modeling, the present paper is focused on two aspects: the proposed model’s performance and the optimization algorithms performance. Results show that among these algorithms, the DE algorithm outperformed its counterpart in most situations. As for the model, the results show us that it performs well in prediction scenarios.

show abstract

“…Due to the computing complexity of Kalman Filter for tiny sensors, we have chosen to apply the polynomial regression with least squares in SDPL motivated by its simplicity to implement and its reduced time processing. In fact, Kalman filter was found of time complexity of O(N 3 ) while the least square (LS) is of complexity of O(N²) [51] thus LS is faster than Kalman filter. In addition, polynomial regression fits a non-linear model to the data even though the regression is linear so it can be applied in larger scenarios.…”

Section: Location Refinementmentioning

confidence: 99%

Intertwined localization and error-resilient geographic routing for mobile wireless sensor networks

2018

View full text Add to dashboard Cite

Geographic routing in wireless sensor networks brings numerous inherent advantages, albeit its performance relying heavily on accurate node locations. In mobile networks, localization of the continuously moving nodes is a challenging task and location errors are inevitable and affect considerably routing decisions. Our proposal is in response to the unrealistic assumption widely made by previous geographic routing protocols that the accurate location of mobile nodes can be obtained at any time. Such idealized assumption results in underperforming or infeasible routing protocols for the real world applications. In this paper, we propose INTEGER, a localization method intertwined with a new location-errorresilient geographic routing specifically designed for mobile sensor networks even when these networks are intermittently connected. By combining the localization phase with the geographic routing process, INTEGER can select a relay node based on nodes' mobility predictions from the localization phase. Results show that INTEGER improves the efficiency of the routing by increasing the packet delivery ratio and by reducing the energy consumption while minimizing the number of relay nodes compared to six prevalent protocols from the literature.

show abstract

A Study about Kalman Filters Applied to Embedded Sensors

Cited by 73 publications

References 17 publications

Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

Seismic Model Parameter Optimization for Building Structures

Intertwined localization and error-resilient geographic routing for mobile wireless sensor networks

Contact Info

Product

Resources

About