Covariance bounds for augmented state Kalman filter application

Deaves, Rob H.

doi:10.1049/el:19991355

Cited by 11 publications

(7 citation statements)

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“…Since this bound is based on the Fisher information matrix, literature on this topic is typically problem specific. Other notable examples of state error covariance bounds for Kalman filtering have been noted in regards to relating observability and controllability [16], augmented state applications [15], and multi-target multi-sensor tracking systems [5,41]. In the latter example, Bishop and Nabba [5] examine the use of the least-squares covariance to serve as a reference for the extended Kalman filter performance, and to study optimal sensor placement for a defense corridor guarding against approaching aircraft.…”

Section: Review Of Existing Literaturementioning

confidence: 99%

Navigation Algorithms and Observability Analysis for Formation Flying Missions

Huxel

2009

Journal of Guidance, Control, and Dynamics

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Navigation algorithms and the corresponding observability analysis for formation flying missions are developed. In particular, the advantages and concerns associated with the use of combinations of inertial and relative measurements are examined. The methodology of the observability analysis relates the physical geometry of the observers, as well as the spacecraft formation, to a measure of linear time-invariant system observability. The investigation also examines the robustness of the extended Kalman filter while simultaneously processing inertial and relative range measurements. It has been shown that processing relative range measurements in conjunction with inertial range measurements can directly increase the accuracy of the inertial state estimate. However, it has also been shown that when there is relatively large uncertainty in the state estimate, the addition of relative measurements can cause an otherwise convergent filter to diverge. This paper considers several methods for preventing this divergence, as well as an in-depth examination of second-order terms to explain the basis of the problem. In particular, to illustrate their potential significance, analytical bounds are derived for the second-order terms

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Section: Review Of Existing Literaturementioning

confidence: 99%

Navigation Algorithms and Observability Analysis for Formation Flying Missions

Huxel

2009

Journal of Guidance, Control, and Dynamics

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“…These filters marginalize out past measurements and estimate only the current state precluding re-linearization degrading estimation quality. To partly overcome these problems, researchers have developed augmented state filters [1] to keep track of some past poses and iterated versions [2] to support re-linearization. Consequently, achieving a better or an optimal solution implies retaining some or all previous states.…”

Section: Introductionmentioning

confidence: 99%

Fixed-wing state level HIL via factor graph incremental smoothing

Rodrigues

Rosa

2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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Many robotics problems such as simultaneous localization and mapping (SLAM) and bundle adjustment (BA) can be formulated as a nonlinear least squares graph optimization. In these strategies, each node in the graph represents a robot position or a sensor measurement obtained at that position. Edges represent constraints between these nodes. The first part of the problem known as front-end is generating the graph. The second part, known as back-end, aims to find a configuration that best explains the constraints usually modeled as error functions. Clearly this is an expensive operation since every movement or measurement adds nodes to the graph that may become optimization hard for limited real-time applications. In order to overcome this problem, an innovative method called incremental smoothing, optimizes only a small subset of the graph nodes, reducing the computational load considerably. Besides, to explore this novel algorithm we propose a state-level hardwarein-the loop (HIL) for a small fixed-wing UAV. It consists of an aerial flight simulator extended with our sensor fusion plugin and the UAV aerodynamic model, an autopilot and a ground control station. MAVLink communications protocol interfaces these modules. Furthermore, we validate our approach with three mission scenarios exploiting different maneuvers ranging from a simple square flight to a much more challenging lawnmower search pattern. Experiments have shown that the proposed method performed satisfactorily.

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“…[7] uses Augmented State Kalman Filter (ASKF) to estimate the correct position of both the vehicle and every frame of the mosaic, based on crossover and displacement measurements [4]. The ASKF strategies for the mosaic update and vehicle localization take into account simplified dynamic model of the AUV, as well as the detected crossover regions, which is very important to the accuracy of the system.…”

Section: Introductionmentioning

confidence: 99%