This paper proposes a two-stage architecture for multi-sensor temporal data fusion. The first stage uses extended Kalman filters to track tokens seen by each sensor, and the second stage links the tokens corresponding to the same real-world event. Two pairs of strategies are presented relating to the initial data association between tokens and filters, together with decision rules for switching...

The objective of the Rotorcraft Pilot's Associate (RPA) Advanced Technology Demonstration (ATD) is to apply artificial intelligence and state-of-the-art computing technologies to manage and integrate next generation mission equipment and battlefield information in order to enhance the lethality, survivability, and mission effectiveness of combat helicopters. Lockheed Martin Advanced Technology ...

Dempster-Shafer Evidence Theory(DST) enables multi-sensor data fusion, which makes it possible to infer the context. In this paper, we propose about how to use multi-sensor data fusion to infer context in the dynamic circumstances. Dynamic circumstance means a changing of the situation or the surrounding itself, and particularly signifies that there is a changeable factor in a specific environm...

In this paper, a homogenous multi-sensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemen...

Multi-sensor management for data fusion in target tracking concerns issues of sensor assignment and scheduling by managing or coordinating the use of multiple sensor resources. Since a centralized sensor management technique has a crucial limitation in that the failure of the central node would cause whole system failure, a decentralized sensor management (DSM) scheme is increasingly important ...

Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deployed in smart environments. However, data coming from sensors are often noisy and inaccurate, and thus probabilistic techniques, such as Dynamic Bayesian Networks, are often adopted to explicitly model the noise and uncertainty of data. This work proposes to improve the accuracy of probabilistic in...

Traffic and mobility are essential ingredients of modern society, as they are important prerequisites for economic and social prosperity. Due to opportunities provided by modern technologies, traffic management has become an important component in improving quality and safety, especially in road traffic. The objective of this paper is to present a novel approach to performing data correlation a...

An optimal mean-square fusion formulas with scalar and matrix weights are presented. The relationship between them is established. The fusion formulas are compared on the continuous-time filtering problem. The basic differential equation for cross-covariance of the local errors being the key quantity for distributed fusion is derived. It is shown that the fusion filters are effective for multi-...