The real-time electromagnetic tracking method has been widely applied, despite its limitations such as a high computational cost and complex hardware components. In this paper, a simple and efficient electromagnetic tracking approach, with both three-dimensional (3D) and 6D pose tracking, is proposed. This approach utilizes the geomagnetic field and the magnetic field generated by the 2.5 kHz sinusoidal signal from a uniaxial transmitting coil. By extracting both the alternating current (AC) and direct current (DC) components from the outputs of a miniature tri-axial magneto-resistive sensor, the position and orientation parameters of the sensor can be computed by using the combination of the geomagnetic orientation algorithm and the magnetic dipole model-based localization algorithm. Since the implementation of the tracking algorithm is based on a sextic equation, this proposed approach is simple and fast. Experimental results show that the root-mean-square value of the position error is 2.6 ±0.79mm and the orientation error is 1.4 ±0.61°. This tracking approach has simple structures of both software and hardware but promising performance and applicability.

Due to the stochastic behaviour of fatigue crack propagation resulting from the multifarious uncertainties such as aleatory uncertainty and epistemic uncertainty, a synthetic scheme integrating heterogeneous uncertainty resources is needed to make the prognosis of fatigue crack propagation be accurate. In this paper, an on-line updating strategy combining an adaptively tunable hybrid radial basis function (RBF) network and active Lamb waves based structural health monitoring is proposed for prognosis of fatigue cracks in an aircraft wing. A particle filter and Brownian motion with drift and proportionality coefficients are put forward to dynamically access a posteriori estimation of crack state and model parameters. Because of the advantages of flexibility, electrically stabilized and suitable for complex structure, the sensor layer with an embedded network of distributed piezoelectric transducers is utilized to collect Lamb wave signals describing crack states on-line. A hybrid spatial phase difference-based damage index (DI) is employed to capture the characteristics of Lamb wave signals to quantitatively determine the crack length. Furthermore, fatigue tests on the outboard wing of a real airplane are conducted to substantiate the proposed method. The experimental results demonstrate that the hybrid RBF model can timely trap the dynamic characteristics of sensor signals, and effectively predict the varying tendency of cracks growth. The uncertainties during the prognosis of fatigue cracking propagation can be effectively dealt with by combining the dynamic adjustability and the DIs-based on-line monitoring technique.