Attitude estimation with feedback particle filter

Zhang, Chi; Taghvaei, Amirhossein; Mehta, Prashant G.

doi:10.1109/cdc.2016.7799104

Cited by 9 publications

(7 citation statements)

References 48 publications

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“…In order to mitigate such problems, different sensor data fusion algorithms can be implemented on-board [ 53 , 54 ]. Most UAV attitude estimation algorithms are based on an extended Kalman filter [ 20 , 55 , 56 , 57 ], unscented Kalman filter [ 58 , 59 ], particle filtering [ 36 , 51 , 60 ], quaternion estimation (QUEST) algorithms [ 61 , 62 ], complementary filter [ 63 , 64 ] and Madgwick filter [ 65 ].…”

Section: Uav Attitude Estimation Problemmentioning

confidence: 99%

See 1 more Smart Citation

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

D’Amato

Nardi

Notaro

et al. 2021

Sensors

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Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

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Section: Uav Attitude Estimation Problemmentioning

confidence: 99%

“…The estimation problem is dealt with by the design of a particle-filter-based sensor fusion algorithm. From a theoretical point of view, a PF could be a realistic option when statistical performance is considered [ 50 , 51 , 52 ], because it is able to deal with non-linear motion models and non-Gaussian noise distributions.…”

Section: Introductionmentioning

confidence: 99%

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

D’Amato

Nardi

Notaro

et al. 2021

Sensors

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“…Proposition 2: Consider the fixed-point problem (20) with the perturbed operator T (ε) defined according to (19). Fix ε > 0.…”

Section: Convergence Analysismentioning

confidence: 99%

Gain function approximation in the feedback particle filter

Taghvaei

Mehta

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-This paper is concerned with numerical algorithms for gain function approximation in the feedback particle filter. The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The problem is to approximate this solution using only particles sampled from the probability distribution. Two algorithms are presented: a Galerkin algorithm and a kernel-based algorithm. Both the algorithms are adapted to the samples and do not require approximation of the probability distribution as an intermediate step. The paper contains error analysis for the algorithms as well as some comparative numerical results for a non-Gaussian distribution. These algorithms are also applied and illustrated for a simple nonlinear filtering example.

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“…We consider the following continuous-time system evolving on a matrix Lie group G, Financial support from the NSF CMMI grants 1334987 and 1462773 is gratefully acknowledged. The conference versions of this paper appear in [61], [62].…”

Section: A Problem Statementmentioning

confidence: 99%

Feedback particle filter on matrix lie groups

Zhang

Taghvaei

Mehta

2016

2016 American Control Conference (ACC)

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This paper is concerned with the problem of continuous-time nonlinear filtering for stochastic processes on a connected matrix Lie group. The main contribution of this paper is to derive the feedback particle filter (FPF) algorithm for this problem. In its general form, the FPF is shown to provide a coordinate-free description of the filter that automatically satisfies the geometric constraints of the manifold. The particle dynamics are encapsulated in a Stratonovich stochastic differential equation that retains the feedback structure of the original (Euclidean) FPF. The implementation of the filter requires a solution of a Poisson equation on the Lie group, and two numerical algorithms are described for this purpose. As an example, the FPF is applied to the problem of attitude estimation -a nonlinear filtering problem on the Lie group SO(3). The formulae of the filter are described using both the rotation matrix and the quaternion coordinates. Comparisons are also provided between the FPF and some popular algorithms for attitude estimation, namely the multiplicative EKF, the unscented quaternion estimator, the left invariant EKF, and the invariant ensemble Kalman filter. Numerical simulations are presented to illustrate the comparisons.

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Attitude estimation with feedback particle filter

Cited by 9 publications

References 48 publications

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Gain function approximation in the feedback particle filter

Feedback particle filter on matrix lie groups

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