The 1/f resistance noise is one of the main noise sources of giant magnetoresistive sensors, which will cause intrinsic detection limit at low frequency. To suppress this noise, a vertical motion flux modulation (VMFM) scheme with high efficiency and simple structures is proposed. And the electrical coupling effect is investigated with an equivalent circuit model. We found that the electrical coupling disturbance can be suppressed by improving the symmetry of VMFM sensors. The modulation efficiency of VMFM sensors has reached 18.8%, which is higher than most prototype sensors with other flux modulation schemes.

A broad linear range of ionic flexible sensors (IFSs) with high sensitivity is vital to guarantee accurate pressure acquisition and simplify back-end circuits. However, the issue that sensitivity gradually decreases as the applied pressure increases hinders the linearity over the whole working range and limits its wide-ranging application. Herein, we design a two-scale random microstructure ionic gel film with rich porosity and a rough surface. It increases the buffer space during compression, enabling the stress deformation to be more uniform, which makes sure that the sensitivity maintains steady as the pressure loading. In addition, we develop electrodes with multilayer graphene produced by a roll-to-roll process, utilizing its large interlayer spacing and ion-accessible surface area. It benefits the migration and diffusion of ions inside the electrolyte, which increases the unit area capacitance and sensitivity, respectively. The IFS shows ultra-high linearity and a linear range (correlation coefficient ∼ 0.9931) over 0-1 MPa, an excellent sensitivity (∼12.8 kPa^-1), a fast response and relaxation time (∼20 and ∼30 ms, respectively), a low detection limit (∼2.5 Pa), and outstanding mechanical stability. This work offers an available path to achieve wide-range linear response, which has potential applications for attaching to soft robots, followed with sensing slight disturbances induced by ships or submersibles.

Traditional geomagnetic vector matching methods are mainly based on a certain correlation criterion to filter the optimal track, that the optimal track selection function is single and unable to distinguish the nonlinear mapping of the geomagnetic field and geographical position. Since the geomagnetic matching process is similar to pattern recognition, a vector pattern recognition matching method based on a probabilistic neural network (PNN) is proposed to realize geomagnetic navigation. The neural network input is geomagnetic vector elements, and the genetic algorithm is used to optimize the PNN’s smooth parameter to classify better. The comparison of VICCP, VMAGCOM and the proposed method is carried out in simulation in two kinds of areas with significant and insignificant geomagnetic features. Simulation results show that the proposed method has the highest matching rates of 94% and 100% in two kinds of regions. The matching accuracy is also significantly better than traditional algorithms. The experiment is carried out to verify the effectiveness and robustness of the proposed method at last.

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.

To improve the sensitivity of the magnetic tunnel junction(MTJ)sensor, a novel architecture for a double-gap magnetic flux concentrator (MFC) was studied theoretically and experimentally in this paper. The three-dimensional finite element model of magnetic flux was established to optimize the magnetic field amplification factor, with different gaps. The simulation results indicate that the sensitivity of an MTJ sensor with a double-gap MFC can be significantly better than that of a sensor with a traditional single-gap MFC, due to the fact that the magnetic magnification sharply increases with the decrease in effective gap width. Besides this, the half-bridge MTJ sensors with the double-gap MFC were fabricated using photolithography, ion milling, evaporation, and electroplating processes. Experimental results show that the sensitivity of the MTJ sensor increased by ten times compared to the sensor without the double-gap MFC, which underlines the theoretical predictions. Furthermore, there is no significant increase in the sensor noise. The work in this paper contributes to the development of high-performance MTJ sensors.