Abstract: The SLAM problem in static environments with EKF is adapted for multi-rate sensor fusion of encoders and laser rangers. In addition, the formulation is general and can be adapted for any multi-rate sensor fusion application. The proposed algorithm, based on well-known techniques for feature extraction, data association and map building, is validated with some experimental results. Thi...

Designing tracking systems that cover large indoor areas and encompass different sensor modalities pose many significant challenges such as multi-sensor data fusion, coordinate system handoff and associated transformations. In this paper, we present the design and implementation of a prototypical system that effectively tackles these challenges. The proposed system is empirically validated in a...

Multi sensor data fusion is the data from multiple sensors and information from the relevant database are combined, which obtained judgment and description that can not achieve the goal, more accurate and complete by any single sensor. BP neural network is a kind of artificial neural network based on error back-propagation algorithm. It adopts adding hidden layer, to estimate the error directly...

By analyzing multi-sensor information fusion system and hall for workshop of meta-synthetic engineering (HWME) essentially, a universal information fusion system of HWME based on multi-sensor is put forward. Analyzing the fault diagnosis framework of complex system based on information fusion technique, together with the research on the general process of information fusion synthesis fault diag...

This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter...

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of loc...

This study attempts to apply a back-propagation network (BPN) for multi-sensors data fusion in a wireless sensor networks (WSNs) system with a node-sink mobile network structure. This investigate is to finish the factory monitoring at environment monitoring services (EMS). These practice wireless sensor network circuits include temperature, humidity, ultraviolet, and illumination four variable ...

Today many tracking and surveillance systems show multi sensor configurations, which are used to enhance the breadth of measurement and likewise to increase the capability of the system to survive if any individual sensor fails. Currently, multi sensor systems rely on a central processor where global data fusion takes place, or a central communication medium through which all messages between s...

This paper proposes a heuristic method for the sensor selection problem that uses a state vector fusion approach as a data fusion method. We explain the heuristic to estimate a stationary target position. Given a first sensor with specified accuracy and by using genetic algorithm, the heuristic selects second sensor such that the fusion of two sensor measurements would yield an optimal estimati...