Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Abstract

The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

“…The AOA is a calculation process of direction between the target device and the fixed stations. The AOA measurements are used to determine the angle at which a target mobile device acquires signals from numerous fixed stations at a known location [36]. To estimate a position in a 2D space, the AOA method only requires two fixed stations.…”

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

“…The localization system can operate normally without a signal or lack of signal measurements due to the change environments. For example, with RSS-based fingerprint technique, if a new given RSSI value is not exist in the RSS Map database, because of obstacles or failures of a part of the localization system, the system should provide accurate location information [71]. Thus, one of the system's most critical features is stability, so precision is often sacrificed to increase system stability.…”

“…Iraq. (email: halgurd.maghdid@koyauniversity.org) smartphones, drones, watch, beacons, and vehicles) using certain fixed nodes and mobile computing devices [1]. Further, the location information can be used in different services including navigation, tracking, monitoring, etc.…”

“…Where, the variety of these techniques is based on Received Signal Strength Indicator (RSSI) signal measurements [16] and Time of Flight (TOF) measurements [17]. The RSSI measurements can be also used in different ways: (1) placement based on the estimated distance between the target device and known fixed stations computed via RSSI.…”

Square-Root Cubature Kalman Filter Based on H∞ Filter for Attitude Measurement of High-Spinning Aircraft

“…In recent years, many scholars have done a lot of research on this phenomenon. Among them, Qiu and Guo [9] introduce the adaptive Huber algorithm based on multiple strong tracking into the standard CKF, which can effectively improve the filtering performance of CKF; Geng et al [10] overcome the influence of CKF model error and abnormal interference by establishing an adaptive factor based on prediction residual; Tang et al [11] use QR decomposition of matrix and quaternion numerical integration to construct a new CKF algorithm, which effectively improves the rate of convergence of filtering; Huang et al [12] proposed a stable and highstrength tracking CKF algorithm to improve the regulation ability of CKF. To reduce the complexity of filtering operation, the QR decomposition method with strong numerical stability is applied to decompose and iterate the system state covariance matrix to ensure numerical stability of matrix operation in the algorithm [13].…”