We present a system for detecting and tracking the lanes of high-speed roads, in order to warn the driver of accidental lane departures. The proposed method introduces a novel variant of the classic Hough transform, better equipped to detect and locate linear road markings with a common vanishing point. This is combined with a simple model of the lane and an Extended Kalman Filter to make detec...

In many autonomous underwater vehicle (AUV) applications, a basic problem is to determine the AUV's position accurately. Terrain aided navigation, which supports the existing inertial navigation system with terrain information, is a promising method. In this thesis, the Kalman filter and the Bayesian approach implemented as point mass filter are used in terrain aided navigation. To formulate a ...

A model-based process fault monitoring approach is proposed in this paper which utilizes a multi-sensor data fusion technique. The fusion algorithm is based on a discrete-time extended Kalman filter (EKF). The presented EKF is modified to incorporate the asynchronous sensor measurements. The resulting approach will be evaluated for a variety of conditions including synchronous/asynchronous meas...

GPS/INS integrated system is very subject to uncertainties due to exogenous disturbances, device damage, and inaccurate sensor noise statistics. Conventional Kalman filer has no robustness to address system uncertainties which may corrupt filter performance and even cause filter divergence. Based on the INS error dynamic equation, a robust Kalman filter is analyzed and applied in loosely couple...

Report [1] compares the Extended Kalman Filter [2, 3, 4], the Iterated Extended Kalman Filter, IEKF, [2, 3, 4] and the Linear Regression Kalman Filter [5] (e.g. the Unscented Kalman Filter, UKF, [6, 7, 8]) on (i) consistency and (ii) information content of their results (estimates and covariance matrices). The nonlinear filter proposed by Bellaire et al. in [9] is not discussed in report [1]. T...

Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environ...

As opposed to the analytic approach used in the modern theory of optimal filtering, a synthetic approach is presented. The signalhensor data, which are generated by either computer simulation or actual experiments, are synthesized into a filter by training a recurrent multilayer perceptron (RMLP) with at least one hidden layer of fully or partially interconnected neurons and with or without out...

The basic problem in Target tracking is to estimate the trajectory of a object from noise corrupted measurements and hence becoming very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filt...

This paper proposes a new radar tracking filter named Noise-estimate Particle Filter (NPF). Kalman filter and particle filter are popular filtering techniques for target tracking. The tracking performance of the Kalman filter severely depends on the setting of several parameters such as system noise and observation noise. However, it is an open problem how to choose proper parameters for variou...

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...