Bhaskara Ramchander Rao, Majji Manoranjan, Guzmán Felipe
Department of Aerospace Engineering, Texas A&M University, College Station, TX 77843, USA.
Wyant College of Optical Sciences, The University of Arizona, Tucson, AZ 85721, USA.
Sensors (Basel). 2024 Oct 1;24(19):6381. doi: 10.3390/s24196381.
This paper investigates state estimation methods for dynamical systems when model evaluations are performed on resource-constrained embedded systems with finite precision compute elements. Minimum mean square estimation algorithms are reformulated to incorporate finite-precision numerical errors in states, inputs, and measurements. Quantized versions of least squares batch estimation, sequential Kalman, and square-root filtering algorithms are proposed for fixed-point implementations. Numerical simulations are used to demonstrate performance improvements over standard filter formulations. Steady-state covariance analysis is employed to capture the performance trade-offs with numerical precision, providing insights into the best possible filter accuracy achievable for a given numerical representation. A low-latency fixed-point acceleration state estimation architecture for optomechanical sensing applications is realized on Field Programmable Gate Array System on Chip (FPGA-SoC) hardware. The hardware implementation results of the estimator are compared with double-precision MATLAB implementation, and the performance metrics are reported. Simulations and the experimental results underscore the significance of modeling quantization errors into state estimation pipelines for fixed-point embedded implementations.
本文研究了在具有有限精度计算元件的资源受限嵌入式系统上进行模型评估时，动态系统的状态估计方法。对最小均方估计算法进行了重新表述，以纳入状态、输入和测量中的有限精度数值误差。针对定点实现，提出了最小二乘批估计、序贯卡尔曼滤波和平方根滤波算法的量化版本。数值模拟用于证明相对于标准滤波器公式的性能改进。采用稳态协方差分析来捕捉与数值精度的性能权衡，从而深入了解给定数值表示可实现的最佳滤波器精度。在现场可编程门阵列片上系统（FPGA-SoC）硬件上实现了一种用于光机械传感应用的低延迟定点加速状态估计架构。将估计器的硬件实现结果与双精度MATLAB实现进行了比较，并报告了性能指标。仿真和实验结果强调了在定点嵌入式实现的状态估计管道中对量化误差进行建模的重要性。
