FPGA-Based Implementation of Reduced-Complexity Filtering Algorithm for Real-Time Location Tracking

Abstract

In recent years, wireless communication applications have been widely used in various industries, such as the popularity of smart phones, large-scale cloud computing platforms, and the Internet of Things (IoT) in smart cities. Positioning techniques have made great progress in terms of positioning accuracy and usability. These approaches gradually infiltrate all aspects of social life from the areas of navigation, aerospace, aviation, military, and natural disaster prevention. Namely, these techniques become an indispensable and important applications in daily life, such as personnel search, location search, traffic management, vehicle navigation, and route planning. Therefore, an adaptive low-complexity location-estimation approach combined alpha-beta (α-β) filtering algorithm with the Kalman filtering (KF) algorithm is proposed and implemented in this article. The proposed tracking approach is based on the α-β filtering algorithm extracted the coefficients of the Kalman gain from the KF algorithm. In addition, in order to enhance the performance and calculating speed, we propose the hardware implementation for KF algorithm and α-β filtering algorithm. The proposed concept is performed by field programmable gate array (FPGA), which has the features of pipeline structure and real-time processing. Under a stationary environment, as compared with the software implementation for location tracking, the proposed FPGA training, decision, and tracking approach not only can achieve the location accuracy close to the KF tracking approach but has much lower computational complexity and provides better performance than that of non-tracking approaches.