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The consensus error calculation of the multi-agent systems (MASs) with system noises and communication noises is considered in this paper. Each agent is modeled by a continuous-time linear time-invariant dynamics and the communication topology is described by an undirected graph. It is supposed that the agent can obtain its own state accurately and receive the state of the neighbor agent with noisy...
In pre-processing of spectral data, advancements are yet to be made to discover the most efficient filter and establish parameters which define efficiency. In this paper, a unique method of using the Kalman filter for filtering the spectral data is proposed. Not only is it found that the performance of the Kalman filter is better but also observed that the filter provides better noise suppression...
Localization is a crucial part in robot navigation. The significance of this issue is to the extent that further achievements in terms of robot control is highly dependent on that. There are various methods addressing this subject, among which the extended Kalman filter is proved to be one of the most successful ones. In classic extended Kalman filter, covariance matrixes of process noise Q and measurement...
Compared with electromagnetic current transducer and electronic current transducer, fiber optic current transducer (FOCT) represents the developing direction of current transducer, which has a series of advantages, such as wide bandwidth, good insulation performance, high measuring accuracy, and so on. FOCT is one kind of new-type precise sensor based on the principle of Faraday magneto-optical phase...
This paper proposes a grid impedance estimation method for distributed generation units using a sequential Monte Carlo method called particle filtering. The theoretical background of this method is reviewed, and system equations are derived. First results for implementation are given in this course of the paper. To the authors' knowledge, this is the first implementation of this filter in grid impedance...
This paper is concerned with state filtering and the parameter estimation problem of noisy Hodgkin-Huxley neuronal model. The Cubature Kalman filter is applied to solve the joint estimation problem as an effective means of dealing with system noise and observation noise. The proposed state filtering method is based on the only measurable variable - membrane potential. In addition, the method is applicable...
We investigated differentiated signal from output of filter widely used in engineering, which is based on moving average method. We determined influences of different parameters on filtered signal error. We provided filter structure, that does not require further heuristic tuning.
A nonlinear Bayesian filter is proposed in this paper for a general nonlinear system of continuous time dynamics and discrete time measurements. In this filter, a transient Fokker-Planck equation solver based on tensor decomposition is used for propagating the conditional state probability density function (PDF) in conjunction with a measurement update via Bayes' rule. This filter is not restricted...
In this paper, the problem of false information attack on and the Kalman filter's defense of state estimation in dynamic multi-sensor systems is investigated from a game theoretic perspective. The relationship between the Kalman filter and the adversary can be regarded as a two-person zero-sum game. Under which condition both sides of the game will reach the Nash equilibrium is investigated in the...
For linear-Gaussian non-deterministic dynamics, that is, systems with non-zero process noise, it is well known that tracklet fusion based on equivalent measurement is optimal only for full communication rate, i.e., if the local posterior probabilities or estimates are communicated and fused after each observation and update time. Despite this constraint, tracklet fusion has become very popular because...
We propose a novel measurement update procedure for orientation estimation algorithms that are based on directional statistics. This involves consideration of two scenarios, orientation estimation in the 2D plane and orientation estimation in three-dimensional space. We make use of the von Mises distribution and the Bingham distribution in these scenarios. In the derivation, we discuss directional...
In our previous work, we compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the angle-only filtering (AOF) problem in 3D using Cartesian coordinates and modified spherical coordinates (MSC) for the relative state vector. We found that the UKF-MSC and EKF-MSC had the best performance in accuracy, the UKF-MSC being slightly better...
Filtering algorithms that use different forms of numerical integration to handle measurement and process non-linearities, such as the cubature Kalman filter, can perform extremely poorly in many applications involving angular measurements. We demonstrate how such filters can be modified to take into account the circular nature of the angular measurements, dramatically improving performance. Unlike...
The Ensemble Kalman Filter (EnKF) is a Kalman based particle filter which was introduced to solve large scale data assimilation problems where the state space is of very large dimensionality. It also achieves good results when applied to a target tracking problem, however, due to its Gaussian assumption for the prior density, the performance can be improved by introducing Gaussian mixtures. In this...
In this paper, we look to address the problem of estimating the dynamic direction of arrival (DOA) of a narrowband signal impinging on a sensor array from the far field. The initial estimate is made using a Bayesian compressive sensing (BCS) framework and then tracked using a Bayesian compressed sensing Kalman filter (BCSKF). The BCS framework splits the angular region into N potential DOAs and enforces...
A target can be positioned by wireless communication sensors. When the range based sensors have biased measurements, an Expectation Maximization (EM) algorithm is proposed to jointly estimate the target state and sensors' biases, including the batch EM and sliding window EM algorithms. To implement the algorithms, the Iterated Extended Kalman Smoother (IEKS) is also embedded in the EM algorithm. The...
A model is made in view of the MEMS gyroscope random error, which is applied to error compensation with the Kalman filter. And main noise sources that affect measurement accuracy are determined via Allan variance method. The correctness of the model is verified by data filtering, proper error model and error compensation of the MEMS gyroscope. The principle factors that affect the performance of MEMS...
Phase information recovered through interferometric techniques is mathematically wrapped in the interval (−π, π). Obtaining the original unwrapped phase is very important in numerous number of applications. This paper discusses a Fourier transform based phase unwrapping method. Kalman filter is proposed for denoising in post processing step to restore the unwrapped phase without any noise. The proposed...
The new tool for off-line estimation of the state of discrete linear systems is presented. The algorithm of Finite Impulse Response (FIR) smoother is described and its optimality is proven. The optimality of this smoother means that error covariance matrix of the estimation is minimal. It places this method in the Least Square Estimation (LSE) methods group, which are much better than frequently used...
In this paper, we introduce a new framework to combine the central difference information filter (CDIF) with the interacting multiple model (IMM) method for maneuvering object tracking. The CDIF has been recently introduced for solving object tracking problem using multiple sensors. The CDIF uses Stirling's interpolation to generate a number of sigma points for approximating the distribution of Gaussian...
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