Design and Evaluation of Sensor Systems for State and Parameter Estimation

Gupta, N. K.; Hall, W. E.

doi:10.2514/3.55802

Cited by 18 publications

(5 citation statements)

References 2 publications

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“…Equation (5) does not make the small field-of-view (FOV) assumption, which is more useful for the VISNAV sensor since it incorporates a wide angle lens. Rather, the assumption leading to Eq.…”

Section: A Vision-based Navigationmentioning

confidence: 99%

“…For example, sensitivity studies on random and systematic errors in sensors provide a mechanism to discovery possible anomalous characteristics or problems at an early stage. 5 Placement of sensor locations is also important to overall estimation performance. The traditional sensor-placement problem is based on static process conditions, which can be extended to linear dynamic processes.…”

Section: Introductionmentioning

confidence: 99%

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Colored-Noise Kalman Filter for Vibration Mitigation of Position/Attitude Estimation Systems

Kumar¹

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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A colored-noise Kalman filter is designed to diminish the error effects caused by sensors placed on vibrating structures. This paper deals with sensors that are used to estimate position, attitude or both. Here we focus on a vision-based system, which uses a set of lightemitting diode beacons with a focal plane detector to determine line-of-sight measurements. Estimation of both position and attitude is possible with this system. Vibrational effects are added to the beacon locations and a colored-noise filter is designed to mitigate the effects of the beacon movements on state estimation. A sensitivity study is conducted for this paper work, where the effects of beacon location errors on the estimation of a vehicle's position and attitude are examined. Beacon location variation is introduced into the standard vision-based navigation problem as second-order vibration noise. Further, an error in the process-noise covariance is assumed and its effect on the estimated quantity is observed. Different magnitudes of vibration are added to the beacons position and the robustness properties of the colored-noise filter is analyzed. Results indicate that the colored-noise filter provides significant improvements over a filter that does not account for vibrational effects.

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Section: A Vision-based Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Colored-Noise Kalman Filter for Vibration Mitigation of Position/Attitude Estimation Systems

Kumar¹

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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“…Substituting Equation (l) into Equation (2), the following equation is obtained: (3) where B = [B 1 , B 2 , ... , B 8 ], and where N i,x = ∂N i /∂x and N i,y = ∂N i /∂y.…”

Section: Finite Element Modeling the Linear Elastic Fieldmentioning

confidence: 99%

“…Optimal locations of actuators are deter-mined by using a progressive structural collapse analogy [2]. Attention is concentrated on the sensor placement and sensor system evaluation with location constraints for a given plant matrix [3]. Minimum control energy [4] is served as the criterion for calculating the optimal actuator location; the optimum location of S/As is determined by the maximum of the structural damping index (SDI) [5].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Determining Locations of the Piezoelectric Sensor/Actuator for Vibration Control of Intelligent Structures

Chen

Cao²

2000

Journal of Intelligent Material Systems and Structures

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A technique for determining the locations of piezoelectric sensors and actuators (S/As) of intelligent structures on the basis of element sensitivities of the singular-values of the controllability and observability is presented. The element sensitivity of the singular-values is used as the measure to identify the best locations of S/As, which is defined from the 1st order perturbations of singular-values of the controllability and observability. A criterion and a threshold value are used to determine which actuator/sensor elements need to be kept/removed based on the sensitivity information. Locations of S/As are determined as the point where the element sensitivity of the singular-values is the maximum. Two numerical examples, the cantilever beam and the model of solar panels, are given to illustrate the applications of the present method.

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“…ML has been widely used due to its unbiasedness [14][15][16][17]. Carnduff and Cooke [18] applied ML to reconstruct the aerodynamic model of an unmanned aerial vehicle (UAV) with an unconventional fuselage structure.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Wang

Xia

2023

International Journal of Aerospace Engineering

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The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. To obtain the aerodynamic parameters of the projectile accurately, an aerodynamic parameter identification model based on ensemble learning theory and ELM optimized by improved particle swarm optimization is proposed. The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. Combined with the ensemble learning theory, the improved ELM neural network is regarded as a weak learner to generate a strong learner. The structural parameters of the strong learner were continuously optimized through training, and an aerodynamic parameter identification model of projectile based on ensemble learning theory is obtained. The simulation results show that the introduction of the IPSO and ensemble learning theory enables the model to exhibit excellent generalization ability. The proposed identification model can accurately describe the variation of aerodynamic parameters with the Mach number.

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Design and Evaluation of Sensor Systems for State and Parameter Estimation

Cited by 18 publications

References 2 publications

Colored-Noise Kalman Filter for Vibration Mitigation of Position/Attitude Estimation Systems

Colored-Noise Kalman Filter for Vibration Mitigation of Position/Attitude Estimation Systems

A New Method for Determining Locations of the Piezoelectric Sensor/Actuator for Vibration Control of Intelligent Structures

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

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