Computational Complexity Comparison Of Multi-Sensor Single Target Data Fusion Methods By Matlab

TL;DR

This paper compares the computational complexity of four multi-sensor data fusion algorithms based on Kalman filters, highlighting the inverse covariance method's efficiency with many sensors and providing insights for selecting appropriate methods.

Contribution

It offers a comparative analysis of four Kalman filter-based data fusion algorithms in terms of computational load as sensor count increases, using MATLAB simulations.

Findings

Inverse covariance method is most efficient with over 20 sensors.

For fewer sensors, group sensors method is more suitable.

Computational performance varies significantly with the number of sensors.

Abstract

Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..