Optimized Parallel Implementation of Extended Kalman Filter Using FPGA

There are enormous numbers of applications that require the use of tracking algorithms to predict the future states of a system according to its previous accumulated states. Thus, many efficient techniques are widely adopted to estimate the future states of a system at every point in time to get the desired performance levels. Kalman filter is a popular and an efficient method for online estimations for linear measurements. Extended Kalman Filter (EKF), on the other hand, is more suited for nonlinear measurements. However, EKF algorithm is well known to be computationally intensive, and may not achieve the strict requirements of real time applications. This issue has motivated researchers to consider the use of parallel processing platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs) to meet the real time requirements. This paper provides an optimized parallel architecture for EKF using FPGA. Our approach exploits many optimization and parallel techniques such as pipelining, loop unrolling, dataflow, and inlining; and utilizes the inherently parallel architecture nature of FPGAs to accelerate the estimation process. Our experimental analyses show that our optimized implementation of EKF can achieve better results when compared to other implementations using GPU and multicore platforms. Moreover, higher performance levels can be achieved when operating on larger data sizes. This is due to our proposed optimization techniques that we have applied, and the exploited inherent parallelism among EKF operations.