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By combining statistical pattern recognition techniques and a classic multiple-model vehicle tracking framework, the presented algorithm improves the performance of fusing GPS outputs and vehicle motion data. The multiple-model tracking framework is used to represent vehicle motion as a combination of several maneuvers, whilst, pattern recognition techniques are proposed to identify the model that...
The basis for map assisted moving target tracking is a correct and up-to-date representation of the environment. In this contribution a method is proposed to model curved structures, e.g. roads or tracks, with cubic spline curves. The unknown model parameters are estimated based on corrupted measurements using a probabilistic approach. In particular, the method presented results in a linear formulation...
We study a direct location estimator for the problem of calculating the positions of multiple sources from measurements made with a moving antenna array. In the first pre-processing step, subspaces are formed from the raw antenna outputs at all positions of the moving array. Then the parameters of interest are directly estimated from a cost function that results from fusing all subspaces. This Subspace...
Most of the existing distributed estimation fusion algorithms rely on the existence of the inverses of the corresponding error covariance matrices, e.g., distributed estimation fusion algorithms based on the information form of the Kalman filter and the optimal weighted least-square (WLS) estimator. Theoretically speaking, the error covariance matrices are only at least positive semi-definite and...
In multisensor systems, the measurements reported by local sensors are usually not time aligned or synchronous due to different data rates. A novel algorithm, based on Kalman filter combined with pseudomeasurement and equivalent bias, is proposed to solve a general bias estimate problem in asynchronous sensors systems. The pseudomeasurement equation of sensor biases is obtained by linearizing the...
Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensors in the sensor network can suffer from both random and systematic bias problems. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to erroneous inferences being made by the network. The drift in this context is defined...
In this paper we evaluate the influence of variations in the input parameters on the output of a multi-sensor tracking algorithm, using simulated data. The tracking algorithm is a classical Kalman filter using a probabilistic data association. The input to the tracker consists of contact files, each file containing all contacts identified for a specific per source / receiver / ping triplet. The input...
In this paper a multi sensor system for so called vulnerable road user recognition is presented, developed within an EU project for road safety improvement. The data fusion concept rests upon the sensors near infrared camera and wireless ranging devices, which are complemetary concerning their physical properties. By means of an object oriented approach using the Kalman Filter the introduced concept...
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