Research On Ultrasonic Location Based On Modal Optimization Kalman Filter

With the development of the Internet of Things, information services based on the processof location (i.e., positioning) have become increasingly available. Services such as intelligentparking lots, intelligent storage and other indoor services require positioning to be highlyaccurate. Furthermore, positioning using the Global Positioning System (GPS) is often affectedby buildings shielding the satellite signal and is subject to error, making it particularly difficultto position accurately indoors. Wireless local area network (WLAN) positioning is a matureindoor location technology but its positioning mode is strongly affected by the environment andits reliability is difficult to ensure. In addition, IEEE802.11specifications do not provideaccurate measurement and control models of transmission power, making it difficult to makemeasurements with higher accuracy.Compared with GPS and WLAN positioning, positioning with an ultrasonic wave hasadvantages in indoor locations, including its simple system structure, inexpensive hardware,high accuracy, and feasible algorithm. However, because an ultrasonic wave can be disturbedby uncertain factors such as temperature, the shape of the detected object changes and noise anderror perturbations are generated during positioning, which can reduce the accuracy andreliability of the positioning.The extended Kalman filter algorithm is an improvement on the Kalman filter algorithm.The Kalman filter algorithm can only be applied to linear systems, and thus a linearapproximation needs to be made for a nonlinear system and then the Kalman filter algorithmapplied to the linearization model. The Kalman filter is an optimal recursive data processingalgorithm that estimates the minimum variance of the signal to be processed using the systemstate equation and observed relation.Adopting empirical mode decomposition (EMD) to optimize the extended Kalman filteralgorithm, this paper effectively controls the error of transition time, thus realizing highlyaccurate indoor positioning. This modal optimization method first decomposes the ultrasonicsignal obtained from the receiving system of the ultrasonic wave by EMD and then removes alarge proportion of the noise while retaining the original characteristics of the signal. Theenvelope of the signal is then obtained employing EMD again to obtain the time that theultrasonic wave signal arrives accurately, and the Kalman filter algorithm finally corrects thetransition time, thus providing highly accurate positioning results.The application of extended Kalman filter algorithm to ultrasonic positioning systems hasdifficulty in meeting the requirements of precision positioning because the algorithm produces anew calculation error when the system is linearized. Modal optimization of the extended Kalman filter algorithm is thus investigated. The received ultrasonic signal is first decomposedby empirical mode decomposition, the intrinsic mode functions that best represent the originalsignal are then selected to restructure the waveform, and the transition time is finally corrected.Meanwhile, the ultrasonic wave velocity can be corrected. Traditional ultrasonic positioningcan also be improved by combining with a radio-frequency module. It is experimentally shownthat the proposed method limits positioning error to within±5cm and within±1cm aftermultiple recursions.