To solve the problem of lacking a reliable positioning source in complex environments such as indoor and underground, a high-precision position and attitude estimation method based on the low-frequency time-varying magnetic field is proposed in this paper. The traditional time-varying magnetic field positioning method requires the magnetic beacon coordinate system to be consistent with the target, which cannot solve the relative attitude angle information of the target and the accuracy is poor. The proposed method realized with the fingerprint matching algorithm overcomes the shortcoming of traditional solutions, which is penetrating, robust, and accurate.According to the Biot–Savart Law, the magnetic field intensity decays with the distance between the target and the magnetic source, and the orientation of the measured magnetic field has a certain relation to orientation from the source to the target. Hence, according to this principle, an improved fingerprint algorithm is introduced. Firstly, the RSSI fitting line of the magnetic beacon is calculated according to the measured magnetic field in space, and the position is estimated by the fingerprint matching algorithm. The attitude can be achieved from the estimated position and magnetic field direction vector model. Furthermore, the disturbing factors of the magnetic beacon positioning system are analyzed and the optimization method is approached to improve system performance.The performance includes the effective distance of a single magnetic beacon, the positioning accuracy, the stability of the proposed approach, and the influence of the magnetic beacon number are verified by the experiment. The effective distance of a single magnetic beacon is 14 m. The result exhibits the positioning error expectation is 0.069 m and attitude error expectation is 2.3°, respectively. The error does not accumulate over time, which has obvious advantages over the traditional magnetic beacon navigation solutions and has high engineering application value.

position and attitude estimation;model of magnetic beacon;underground and indoor navigation;fingerprint matching algorithm