Multi-sensor distributed fusion estimation with applications in networked systems

Distributed H∞ state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case

Recursive filtering with random parameter matrices, multiple fading measurements and correlated noises

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this study, a bibliographical review on distributed Kalman filtering (DKF) is provided. The study contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2012.0732

Distributed Kalman filtering: a bibliographic review

This paper presents a novel Kalman filter for the accurate determination of a vehicle’s attitude (pitch and roll angles) using a low-cost MEMS inertial measurement unit (IMU) sensor, comprising a tri-axial gyroscope and a tri-axial accelerometer. Currently, vehicles deploy expensive gyroscopes for attitude determination. A low-cost MEMS gyro cannot be used because of the drift problem. Typically, an accelerometer is used to correct this drift by measuring the attitude from gravitational acceleration. This is, however, not possible in vehicular applications, because accelerometer measurements are corrupted by external accelerations produced due to vehicle movements. In this paper, we show that vehicle kinematics allow the removal of external accelerations from the lateral and vertical axis accelerometer measurements, thus giving the correct estimate of lateral and vertical axis gravitational accelerations. An estimate of the longitudinal axis gravitational acceleration can then be obtained by using the vector norm property of gravitational acceleration. A Kalman filter is designed, which implements the proposed solution and uses the accelerometer in conjunction with the gyroscope to accurately determine the attitude of a vehicle. Hence, this paper enables the use of extremely low-cost MEMS IMU for accurate attitude determination in vehicular domain for the first time. The proposed filter was tested by both simulations and experiments under various dynamic conditions and results were compared with five existing methods from the literature. The proposed filter was able to maintain sub-degree estimation accuracy even under very severe and prolonged dynamic conditions. To signify the importance of the achieved accuracy in determining accurate attitude, we investigated its use in two vehicular applications: vehicle yaw estimate and vehicle location estimate by dead reckoning and showed the performance improvements obtained by the proposed filter.

Accurate Attitude Estimation of a Moving Land Vehicle Using Low-Cost MEMS IMU Sensors

This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.

/pdf/globally-optimal-multisensor-distributed-random-parameter-3j1hhx8w5f.pdf

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications

In the multiple sensor/sub-processor system, distributed estimation fusion based on the two level optimization strategy (optimal sensor estimations and optimal processor center fusion) are used widely. Optimal distributed estimation fusion with out-of-sequence measurements (OOSM) at local sensors is presented in this paper, its performance is equivalent to that of the corresponding Kalman filtering using all sensor observations (which is called the centralized Kalman filtering fusion).

Distributed Multisensor Estimation Fusion with Out-of-Sequence Measurements

In the multisensor distributed fusion systems, observations produced by sensors typically arrive at local processors out of sequence. The resulting problem at the central processor/fusion center-how to update current estimate using multiple local out-of-sequent-measurement (OOSM) updates-is a nonstandard distributed estimation problem. In this paper, based on three update algorithms with "Out-of-Sequence" measurement (OOSM), we propose three optimal distributed fusion updates with local OOSM updates, which are, under some regularity conditions, equivalent to the centralized updates with all same lag time OOSMs respectively.

/pdf/optimal-distributed-fusion-update-with-same-lag-time-out-of-1km2xahj48.pdf

Donghua Wang

Papers

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications

Distributed Multisensor Estimation Fusion with Out-of-Sequence Measurements

Optimal Distributed Fusion Update with Same Lag Time Out-of-Sequence Measurements