Architectural Exploration for Energy-Efficient Fixed-Point Kalman Filter VLSI Design

Abstract

Efficient Kalman filter (KF) designs for real-time mobile applications, such as nano-drones navigation, robots localization, spacecraft orbit control, GPS positioning, image recognition, and multisensor data fusion for wearable systems, are key technology goals. The KF is a compute-intensive kernel composed of consecutive complex matrix operations, like multiplications and matrix inversions. The most complex block in the KF is the Kalman gain (KG) function, which involves matrices inversion at each iteration, applying the determinant matrix calculation and division operations. In this article, we combine architectural solutions of different types, for which balancing conflicting low-power and high-performance requirements aiming at real-time KF processing is a key design issue. The key finding in our architectural exploration herein presented is that the KF architectures in semiparallel and sequential forms offer the best balance of circuit area size, power dissipation, and processing speed. Compared to the state-of-the-art solutions, our KF architecture is more efficient, with 2.8 times fewer arithmetic operators, requiring 3.3 times fewer clock cycles. The usefulness of the developed KF in digital signal processing (DSP) is shown herein by simulations of system identification, noise elimination, and state estimation applications. These figures highlight the results of the KF architecture: the speed of adaptation for the system identification applications with root mean square error (RMSE) of 0.01 after 12 samples, precision level in noise elimination applications with RMSE of 0.13, and reliability in state estimation processes with RMSE less than 10% of system peak response.