Sensor data fusion using Mahalanobis distance and single linkage algorithm

Abstract

Multi-sensor data fusion fuses the output from two or more devices that contain sensor or sensor groups and retrieve one or more particular properties of the environment. Commonly used sensors for robotic control include video cameras, range finders, sonar sensors, infrared sensors, tactile sensors, torque sensors, and proximity sensors. Since the measurements obtained by the sensors are uncertain due to noise and accuracy, the sensor data is not always reliable. So, directly using this data may cause inaccurate, even wrong actions, for systems. This paper discusses sensor fusion using Mahalanobis distance single linkage algorithm. The data fusion here involves testing sensor data closeness, merging close sensor data and optimizing the close sensor data. Mahalanobis distance technique is used to test sensor data closeness, the single linkage algorithm is used for merging close sensors, and the maximum likelihood estimation method is applied for optimizing close sensor data. In this paper we show how to apply these well known mathematical techniques for general sensor data fusion. >