Fast Evaluation Scheme For MEMS IMU Navigation Performance Based On Emulation Method

With the development of micro-electro-mechanical technology and the surge in demand for consumer-level navigation applications,MEMS IMU has ushered in the era of blowout development.At the same time,the emerging of a variety of MEMS IMU products on the market makes reliable and efficient navigation performance evaluation and screening of MEMS devices an urgent need.Traditional IMU navigation performance evaluation methods are difficult to balance the evaluation accuracy and the efficiency and cost,so a fast and effective evaluation scheme for MEMS IMU navigation performance is urgently needed.Hence,this paper designed an efficient MEMS IMU navigation performance evaluation scheme,and verified its accuracy and reliability with field data as reference.Furthermore,based on this scheme and the corresponding software implementation,the influence of dynamic error and temperature sensitivity on MEMS IMU navigation performance is analyzed.This paper firstly made an introduction of the theories in inertial navigation technology and GNSS/INS integrated navigation technology,which is related to the evaluation of IMU navigation performance.Then an emulation method based on IMU signal grafting is designed.This emulation method makes use of the reference IMU signals in the field test as the true kinematic inertial signals,and uses the MEMS static/quasi-static signals collectd in laboratories as the MEMS sensor errors.Next,MEMS static error signals are grafted onto the reference field data to obtain a quasi-field dataset for the MEMS IMU.Meanwhile,this paper sorted out a MEMS IMU navigation performance evaluation process(including implementation steps,practical points and evaluation standards),and completed the corresponding sofrware development and evaluation.In this paper,the principle,method and overall scheme of the designed fast evaluation scheme for MEMS IMU navigation performance are elaborated in detail;the advantages and disadvantages of this fast evaluation scheme are fully analyzed and discussed;three different grades of MEMS IMU field data are used as a reference to make a fully verification on the accuracy and reliability of the fast evaluation scheme,covering two typical integrated navigation modes,namely GNSS/INS integrated navigation mode and vehicle auxiliary information attached mode(ie.vehicle motion constraint and odometer,NHC/ODO).It is showed by the verification results that,on the key indicators of navigation performance(such as the horizontal position error),the conformity between the evaluation results of the fast evaluation scheme and field tests is better than 60% for GNSS/INS integratd navigation mode,and better than 70% for INS/NHC/ODO mode,showing obvious improvement compared with the traditional data simulation methods in the accuracy of evaluation results.Based on this fast evaluation scheme and its software implementation,it was found for GNSS/INS integrated navigation mode that,with grafting non-orthogonality error onto reference data,the conformity of horizontal position error between fast evaluation scheme and field tests is improved by more than 20% for ADIS16465;while with grafting bias temperature sensitivity error,the conformity of horizontal position error is improved by approximately 10% and height error by approximately 60% for ICM20602,and over 30% improvement on the horizontal position error conformity was achieved for ASM330 LHH.It is showed by comprehensive experimental verification that,the fast evaluation scheme for MEMS IMU navigation performance given in this paper is a reliable and effective solution,which takes into account the accuracy,efficiency and the cost.It makes important practical significance for the MEMS IMU selection requirements which is in company with the blowout development of MEMS IMU products,and also provides a new method for studying the quantitative impact of certain MEMS IMU error factors on navigation performance.The signal grafting method adopted in this paper is versatile and can be extended to the navigation performance evaluation of other navigation sensors.