Optimal Information Fusion Research Articles

Multi-robot SLAM via Information Fusion Extended Kalman Filters

Abstract: This paper is concerned with Simultaneous Localization and Mapping (SLAM) problem with multiple mobile robots. Each robot detects landmarks and other robots, and estimates their positions by the extended Kalman filters. To achieve good estimation accuracy, an optimal information fusion technique is adapted to the multi-robot SLAM problem. This technique involves the minimization of the estimation error covariance by weighted averaging of the state estimates from the extended Kalman filters. Simulation and experimental results are included to show the effectiveness of the present method.

Robust interacting multiple model algorithms based on multi-sensor fusion criteria

This paper is concerned with the state estimation problem for a class of Markov jump linear discrete-time stochastic systems. Three novel interacting multiple model (IMM) algorithms are proposed based on the H∞ technique, the correlation among estimation errors of mode-conditioned filters and the multi-sensor optimal information fusion criteria. Mode probabilities in the novel algorithms are derived based on the error cross-covariances instead of likelihood functions. The H∞ technique taking the place of Kalman filtering is applied to enhance the robustness of the new approaches. Theoretical analysis and Monte Carlo simulation results indicate that the proposed algorithms are effective and have an obvious advantage in velocity estimation when tracking a maneuvering target.

A method for improving the pose accuracy of a robot manipulator based on multi-sensor combined measurement and data fusion.

An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results.

Comparison of various approaches to multi-channel Information fusion in c-otdr systems for remote monitoring of extended objects

The paper presents new results concerning selection of optimal information fusion formula for ensembles of COTDR channels. Here C-OTDR is a coherent optical time domain reflectometer. Each of these channels provides data for appropriate automatic classifier which is designed to classify the elastic vibration sources in the multiclass case. Those classifiers form a so-called classifiers ensemble. Ensembles of Lipschitz Classifiers were considered. In this case the goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The Matching Pursuit Optimization Ensemble Classifiers (MPOEC), the Linear Programming Boosting (LP-Boost) (LP-β and LP-B variants), the Multiple Kernel Learning (MKL), and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. The basics of these methods have been briefly described along with intrinsic features. All of those methods are based on reducing the task of choosing convex hull parameters to a solution of an optimization problem. All of the mentioned approaches can be successfully used for using in the C-OTDR system data processing. Results of practical usage are presented.

Information Fusion Predictive Control Algorithm for Time-Varying Systems with Unknown Stochastic System Bias

This paper puts forward on a fast distributed information fusion predictive control algorithm for the time-varying system with unknown stochastic system bias. It is based on the distributed fusion estimation algorithms and state-space model. The optimal information fusion rule for this algorithm is weighted by matrices, diagonal matrices and scalars. It can avoid the complicated Diophantine equation, thus obviously reduces the amount of calculation. Via the distributed information fusion algorithm, the comparison of algorithm in this paper with the local sensor, this algorithm improves stability and accuracy for the timevarying system with unknown stochastic system bias. By testing through the three-sensor target tracking control system simulation, this algorithm shows its effectiveness and correctness, and the results of simulation also show no significant difference in error between the three kinds of distributed fusion algorithm. With reduction of calculation using the scalar weighting fusion predictor, the information fusion estimation algorithm presented in this paper also improves the calculation speed and accuracy.

Self‐tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi‐channel autoregressive moving average signals

SummaryFor the multi‐sensor multi‐channel autoregressive (AR) moving average signals with white measurement noises and an AR‐colored measurement noise, a multi‐stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multidimensional recursive instrumental variable algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. They have the strong consistency. Substituting them into the optimal information fusion Kalman filter weighted by scalars, a self‐tuning fusion Kalman filter for multi‐channel AR moving average signals is presented. Applying the dynamic error system analysis method, it is proved that the proposed self‐tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example for a target tracking system with three sensors shows its effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

Asynchronous Multi-Sensor Fusion Algorithm Based on the Steady-State Kalman Filter

The paper studies an asynchronous multi-sensor fusion problem based a kind of asynchronous multi-sensor dynamic system. Firstly, this paper presents a centralized fusion algorithm based on the Kalman filter without ignoring the correlation between process noise and augmented measurement noise. It is optimal in minimum mean square error. Then using the steady-state Kalman filter to estimate and fuse. Secondly, in the condition that the local sensor estimation error is associated, a distributed fusion algorithm is given by utilizing S.L. Sun optimal information fusion criterion in minimum error covariance matrix trace at fusion center. In distributed algorithm, the value transmitting to the fusion center is determined by the local sensor estimation based on the steady-state Kalman filter and one step predictive value. Since both optimal fusion algorithm standards are different, so the fusion precision will vary. Finally the effectiveness of the algorithm is verified by computer simulation.

An Information Fusion Algorithm Based on Kalman Filtering

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

Design of MEMS Wireless Air-Mouse System Based on MEMS Technology

In this paper, we study the MEMS wireless air-mouse system(MWAMS), which based on three-axis gyroscope、three-axis accelerometer、magnetometer and wireless RF technology. It makes use of MEMS motion sensing technology、information fusion algorithm and DCM complementary filter algorithm to sensing air-mouse's motion. This paper presents a new multi-sensor optimal information fusion and mapping algorithm. Compared to the traditional algorithm, this new algorithm based on MEMS technology not only has higher performance-higher location accuracy and better smoothness, but also has lower power consumption.

Information fusion in navigation systems via factor graph based incremental smoothing

This paper presents a new approach for high-rate information fusion in modern inertial navigation systems, that have a variety of sensors operating at different frequencies. Optimal information fusion corresponds to calculating the maximum a posteriori estimate over the joint probability distribution function (pdf) of all states, a computationally-expensive process in the general case. Our approach consists of two key components, which yields a flexible, high-rate, near-optimal inertial navigation system. First, the joint pdf is represented using a graphical model, the factor graph, that fully exploits the system sparsity and provides a plug and play capability that easily accommodates the addition and removal of measurement sources. Second, an efficient incremental inference algorithm over the factor graph is applied, whose performance approaches the solution that would be obtained by a computationally-expensive batch optimization at a fraction of the computational cost. To further aid high-rate performance, we introduce an equivalent IMU factor based on a recently developed technique for IMU pre-integration, drastically reducing the number of states that must be added to the system. The proposed approach is experimentally validated using real IMU and imagery data that was recorded by a ground vehicle, and a statistical performance study is conducted in a simulated aerial scenario. A comparison to conventional fixed-lag smoothing demonstrates that our method provides a considerably improved trade-off between computational complexity and performance.

Distributed multi-sensor particle filter for bearings-only tracking

In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer–Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.

Two-Sensor Information Fusion with Out-of-Sequence Measurement

In sensor tracking system, Out-Of-Sequence Measurement(OOSM) always occur due to communication delays. The system needs to update the OOSM in order to improve the precision. An optimal information fusion estimation weighted by scalars is presented for two-sensor information fusing with OOSM, which considers correlation between local errors, and avoid to compute the weighted matrix. It is easy to apply in real-time.

Self-tuning decoupled information fusion Wiener state component filters and their convergence

For the multisensor systems with unknown noise variances, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances are obtained, and under linear minimum variance optimal information fusion criterion weighted by scalars for state components, a class of self-tuning decoupled fusion Wiener filters is presented. It realizes the self-tuning decoupled local Wiener filters and self-tuning decoupled fused Wiener filters for the state components. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. The dynamic error system analysis (DESA) method is presented, by which the problem of convergence in a realization for self-tuning fusers is transformed into the stability problems of non-homogeneous difference equations, and the decision criterions of the stability are also presented. It is strictly proved that if the parameter estimation of the MA innovation models is consistent and if the measurement process is bounded in a realization or with probability one, then the self-tuning fusers will converge to the optimal fusers in a realization or with probability one, so that they have the asymptotic optimality. They can deal with the systems with the non-stationary or Gaussian measurement processes. They can reduce the computational burden, and are suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows their effectiveness.

Information fusion white noise deconvolution estimators for time-varying systems

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. Based on the Kalman filtering method and the optimal information fusion rules in the linear minimum variance sense, three distributed fused white noise deconvolution estimators weighted by matrices, diagonal matrices, and scalars, are presented for the linear discrete time-varying stochastic systems with multisensor and with different local dynamic models, respectively. The accuracy of the fusers is higher than that of each local white noise estimator. They can handle the white noise fused filtering, smoothing and prediction problems, and are applicable to the multisensor systems with colored measurement noises. In order to compute the optimal weights, the new formula of computing the local estimation error cross-covariances is presented, and the steady-state white noise deconvolution fusers are also presented, which can reduce the on-line computational burden. Two Monte Carlo simulation examples for the Bernoulli–Gaussian input white noise show their effectiveness.

Asynchronous multirate multisensor information fusion algorithm

This paper is concerned with the optimal dynamic information fusion problem for asynchronous multirate multisensors. By establishing the state space models at each scale, a novel algorithm is developed to recursively fuse the data from multisensors, where the ratio between the sampling rates of different sensors is allowed to be any positive integer. Without using the traditional interpolation or augmentation approaches for states or measurements, the state estimate is obtained based on global measurements, and the obtained state estimate is then proven to be the optimal in the sense of linear minimum variance. It is shown that our main results improve and extend the existing information fusion algorithms for which the sampling rate ratio of different sensors is restricted to one or a power of two. Finally, the feasibility and efficiency of the proposed algorithm is illustrated by a numerical simulation example.

Optimal and self-tuning information fusion Kalman multi-step predictor

Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed optimal information fusion for the steady-state Kalman multi-step predictor is given for discrete linear stochastic control systems with multiple sensors and correlated noises, where the same sample period is assumed. When the noise statistics information is unknown, the distributed information fusion estimators for the noise statistics parameters are presented based on the correlation functions and the weighting average approach. Further, a self-tuning information fusion multi-step predictor is obtained. It has a two-stage fusion structure. The first-stage fusion is to obtain the fused noise statistics information. The second-stage fusion is to obtain the fused multi-step predictor. A simulation example shows the effectiveness.

Self-tuning Information Fusion Kalman Predictor Weighted by Diagonal Matrices and Its Convergence Analysis

For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, estimators of the noise variances are obtained, and under the linear minimum variance optimal information fusion criterion weighted by diagonal matrices, a self-tuning information fusion Kalman predictor is presented, which realizes the self-tuning decoupled fusion Kalman predictors for the state components. Based on the dynamic error system, a new convergence analysis method is presented for self-tuning fuser. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is strictly proved that if the parameter estimation of the MA innovation models is consistent, then the self-tuning fusion Kalman predictor will converge to the optimal fusion Kalman predictor in a realization, or with probability one, so that it has asymptotic optimality. It can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.

OPTIMAL FUSION DISTRIBUTED FILTER FOR DISCRETE MULTICHANNEL ARMA SIGNALS

Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, an optimal information fusion distributed Kalman filter is given for discrete multi-channel ARMA (autoregressive moving average) signals with correlated noises. When all subsystems have the steady-state filters, a steady-state information fusion filter is also given. It has the reduced computation burden compared with the optimal fusion filter. The precision of the fusion filter is higher than that of any local filter, but is lower than that of the centralized filter. The filtering error cross-covariance matrix between any two subsystems is given for discrete multichannel ARMA signals. Applying it to a four-channel ARMA signal system with three sensors shows its effectiveness.

Objectively adaptive image fusion

Signal-level image fusion has been the focus of considerable research attention in recent years with a plethora of algorithms proposed, using a host of image processing and information fusion techniques. Yet what is an optimal information fusion strategy or spectral decomposition that should precede it for any multi-sensor data cannot be defined a priori. This could be learned by either evaluating fusion algorithms subjectively or indeed through a small number of available objective metrics on a large set of relevant sample data. This is not practical however and is limited in that it provides no guarantee of optimal performance should realistic input conditions be different from the sample data. This paper proposes and examines the viability of a powerful framework for objectively adaptive image fusion that explicitly optimises fusion performance for a broad range of input conditions. The idea is to employ the concepts used in objective image fusion evaluation to optimally adapt the fusion process to the input conditions. Specific focus is on fusion for display, which has broad appeal in a wide range of fusion applications such as night vision, avionics and medical imaging. By integrating objective fusion metrics shown to be subjectively relevant into conventional fusion algorithms the framework is used to adapt fusion parameters to achieve optimal fusion display. The results show that the proposed framework achieves a considerable improvement in both level and robustness of fusion performance on a wide array of multi-sensor images and image sequences.

Optimal multi-sensor Kalman smoothing fusion for discrete multichannel ARMA signals

Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, using white noise estimators, an optimal fusion distributed Kalman smoother is given for discrete multi-channel ARMA (autoregressive moving average) signals. The smoothing error cross-covariance matrices between any two sensors are given for measurement noises. Furthermore, the fusion smoother gives higher precision than any local smoother does.