Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation

Abstract

This article puts forward a low-complexity filtering algorithm to achieve a low-complexity filtering chip design for real-time location tracking. In order to meet the need for low-complexity and real-time, the positioning tracking algorithm based on Kalman Filter (KF) is proposed. The KF itself has the functions of tracking, predicting, etc., which can correct the positioning into more accurate results. However, in the calculation of KF algorithms, each iteration often requires tedious and complex calculations of Kalman Gain (KG). Both software and hardware are very resource-intensive. Therefore, use the feature of KG in alpha-beta (α-β) filtering algorithm which can gradually balance in each iteration. Proposed a filtering algorithm that is based on low-complexity, low cost, and high efficiency. This algorithm uses DKF (Difference Kalman Filter) and PKF (Percentage Kalman Filter) depending on different environments. In other words, DKF and PKF are the algorithms which are generated based on different judging conditions. This algorithm can not only significantly reduce the time and the complexity of computing, but also greatly shorten the circuit area of the original algorithm. This algorithm has a large number of matrix operation. In the hardware calculation process, it solves matrix problems about hardware and then developed chip design. Coefficients are used by a multiple of 2 for operation. Use shifters instead of multipliers and dividers, significantly reducing complexity and circuit area. At the same time, deal with the problem of a floating-point number, achieve circuit function verification on the FPGA, and finally tape-out. The design uses the TSMC 0.18μm CMOS cell library provided by the TSRI, uses EDA to implement VLSI with the Design Vision of SYNOPSYS, the operating frequency of the circuit is 83.33 MHz, the value of gate counts is 22.84 K, the power consumption is 3.86 mW, and chip area is 582.63 μm × 580.23 μm.