Intelligent Based Tracking for Underground Mining Using Fuzzy Logic Approaches

A framework for tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional. This is of utmost importance for high performance real -time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter based algorithms. Here the use of non-linear models and non-Gaussian noise is the main explanation for the improvement in accuracy. Tracking, where another object's position is to be estimated based on measurements of relative range and angles to one's own position. Based on simulations, we also argue how the particle filter can be used for tracking based on Monte Carlo methods for tracking the objects. Finally, the particle filter enables a promising solution to the tracking. With possible application to robot localization. In future we implement in hardware using RF technology.