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...

In algorithms of nonlinear Kalman filter, the so-called extended Kalman filter algorithm actually uses first-order Taylor expansion approach to transform a nonlinear system into a linear system. It is obvious that this algorithm will bring some systematic deviations because of ignoring nonlinearity of the system. This paper presents two extended Kalman filter algorithms for nonlinear systems, c...

this paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. the method utilizes a robust extended kalman filter approach to provide an efficient dynamic data reconciliation. a weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. to enhance the...

This paper presents a set of filters with globally exponentially stable error dynamics for source localization and navigation, in 3-D, based on directionmeasurements from the agent (or vehicle) to the source, in addition to relative velocity readings of the agent. Both the source and the agent are allowed to have constant unknown drift velocities and the relative drift velocity is also explicit...

This paper presents the detection of 3D pose of moving humans heads. The contribution of this work is the conjunction of an e ective preprocessing method and a strong estimation Kalman technique which uses less noise sensitive 3D line observations. The procedure involves the detection of the target face in any type of background using color histograms. To outline the face we use a minimization ...

Abstract This paper builds on the filtering work presented in [1]. Our hybrid-state extended Kalman filter [1] is suitable for track-level processing [2] to maintain track through move-stop-move motion patterns, but is inadequate for processing report-level data. Similarly, a direct IMM approach to move-stopmove filtering appears inadequate, since it may lead to excessive track breaks and to ov...

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demon...

This paper addresses the problem of implementing simultaneous localisation and map building (SLAM) in very large outdoor environments. A method is presented to reduce the computational requirement from ~O(N) to ~O(N), being N the states used to represent all the landmarks and vehicle pose. With this implementation the memory requirement are also reduced to ~O(N). This algorithm presents an effi...

Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magne...