面向FPGA的惯性导航算法的研究与实现
|
摘要
针对当前飞控系统中传感器多、运算量庞大,传统微处理器已难以满足其日益增长的性能需求,本文提出了一种面向FPGA的并行化惯性导航算法.本算法采用扩展卡尔曼滤波对结果做最优估计,四阶龙格库塔算法进行积分估计,同时将惯性导航算法进行模块化分割,采用流水线并行处理,提高解算速度.利用FPGA开发板驱动传感器,并在该FPGA上实现对数据的解算,解算结果由接口传输给微处理器,降低了对微处理器接口和性能的要求.最后仿真结果表明,本算法在速度和精度上均有良好的表现,验证了该算法在FPGA上实现的可行性.
Aiming at the problem that the sensor in the current flight control system is to many and the computational complexity is large,the traditional microprocessor has been difficult to meet its growing performance requirements.This paper presents a parallel adaptive inertial navigation algorithm for FPGA.In this algorithm,the extended Kalman filter is used to estimate the result.The fourth-order Runge-Kutta algorithm is used to estimate the integral.At the same time,the inertial navigation algorithm is modularized and the parallel processing is used to improve the solution speed.The FPGA development board is used to drive the sensor,and the data is solved on the FPGA.The result of the solution is transmitted from the interface to the microprocessor to reduce the interface and performance requirements of the microprocessor.Finally,the simulation results show that the proposed algorithm has good performance in terms of speed and accuracy,and verifies the feasibility of the algorithm on FPGA.
Aiming at the problem that the sensor in the current flight control system is to many and the computational complexity is large,the traditional microprocessor has been difficult to meet its growing performance requirements.This paper presents a parallel adaptive inertial navigation algorithm for FPGA.In this algorithm,the extended Kalman filter is used to estimate the result.The fourth-order Runge-Kutta algorithm is used to estimate the integral.At the same time,the inertial navigation algorithm is modularized and the parallel processing is used to improve the solution speed.The FPGA development board is used to drive the sensor,and the data is solved on the FPGA.The result of the solution is transmitted from the interface to the microprocessor to reduce the interface and performance requirements of the microprocessor.Finally,the simulation results show that the proposed algorithm has good performance in terms of speed and accuracy,and verifies the feasibility of the algorithm on FPGA.
引文
[1]Gerd Grau,Elisha J F,Vivek Subramanian.Printed unmanned aerial vehicles using paper-based electroactive polymer actuators and organic ion gel transistors[EB/OL].[2017-10-15].http∥www.nature.com/artides/micronano 201632?proof=twoe#f1.
[2]刘倩,梁志海,范慧芳.浅谈无人机遥感的发展以行业运用[J].测绘与空间地理信息,2016,39(6):167-169.
[3]Dryanovski Ivan,Valenti R G,Xiao J.An opensource navigation system for micro aerial vehicles[J].Autonomous Robots,2013,34(3):177-188.
[4]高礼伟,朱小刚,陶波.基于STM32的小型四旋翼飞控系统的研究与设计[J].科技风,2017,29(6):21-22.
[5]万晓凤,康利平,余运俊,等.互补滤波算法在四旋翼飞行器姿态解算中的应用[J].测控技术,2015,34(2):8-11.
[6]景丽.基于卡尔曼滤波组合导航算法的计算量与精度分析[D].哈尔滨:哈尔滨工业大学,2014.
[7]秦永元.惯性导航[M].北京:科学出版社,2006:287-304.
[8]王励扬,翟昆朋,何文涛,等.四阶龙格库塔算法在捷联惯性导航中的应用[J].计算机仿真,2014,31(11):56-59.
[9]徐泽远,伊国兴,魏振楠.MEMS-SINS/GPS组合导航系统设计[J].自动化仪表,2016,37(12):67-70.
[10]Bhogadi L R,Gottapu S B R,Konala V R.MicroBlaze implementation of GPS/INS integrated system on Virtex-6FPGA[J].Springerplus,2015,4(1):629.
[2]刘倩,梁志海,范慧芳.浅谈无人机遥感的发展以行业运用[J].测绘与空间地理信息,2016,39(6):167-169.
[3]Dryanovski Ivan,Valenti R G,Xiao J.An opensource navigation system for micro aerial vehicles[J].Autonomous Robots,2013,34(3):177-188.
[4]高礼伟,朱小刚,陶波.基于STM32的小型四旋翼飞控系统的研究与设计[J].科技风,2017,29(6):21-22.
[5]万晓凤,康利平,余运俊,等.互补滤波算法在四旋翼飞行器姿态解算中的应用[J].测控技术,2015,34(2):8-11.
[6]景丽.基于卡尔曼滤波组合导航算法的计算量与精度分析[D].哈尔滨:哈尔滨工业大学,2014.
[7]秦永元.惯性导航[M].北京:科学出版社,2006:287-304.
[8]王励扬,翟昆朋,何文涛,等.四阶龙格库塔算法在捷联惯性导航中的应用[J].计算机仿真,2014,31(11):56-59.
[9]徐泽远,伊国兴,魏振楠.MEMS-SINS/GPS组合导航系统设计[J].自动化仪表,2016,37(12):67-70.
[10]Bhogadi L R,Gottapu S B R,Konala V R.MicroBlaze implementation of GPS/INS integrated system on Virtex-6FPGA[J].Springerplus,2015,4(1):629.