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Foot-mounted Inertial Pedestrian Position System (FIPPS) plays an important role for indoor position application. It can be used in the environment without GNSS such as firefighting and military. However, the accumulating position error produced by the MIMU measurement noise, makes the system for long time position impossible. Zero Velocity Update (ZUPT) is a proposed algorithm to reduce position error for FIPPS. For ZUPT, Kalman filter is used to estimate and compensate the position error. However, because the heading misalignment angle cannot be observed by ZUPT, part of the position error caused by heading misalignment angle cannot be compensated. According to the problem above, this paper shows the improving FIPPS position algorithms we proposed in recent years, including: a) Adaptive Gradient Descent Fast-Initial Alignment Algorithm for solving the problem of the inaccuracy initial attitude resulting in the position error; b) the FR-data algorithm for solving the stable problem when the pedestrian walking fast; c) Adaptive Inertial/Magnetometer Positioning Algorithm and Improved Attitude Algorithm for improving the heading misalignment angle observability; d) Dual-foot positioning algorithm based on Adaptive Inequality Constraints Kalman filter for correcting the position error. Finally, performance of the FIPPS improve algorithm is tested using MTi-G710 MIMU.

In order to improve the accuracy of surface ship navigation system, the fully autonomous navigation technique for the integrated navigation using the marine inertial navigation system (INS) and star sensor is used. This method employs navigation information from a star sensor as a benchmark to perform periodic recalibration of the divergent positioning error of INS. However, the position calculation of star sensor needs horizontal attitude information provided by INS. Therefore, the inertial horizontal attitude error is coupled to the star sensor calculation, which results in inaccurate description of the system model. To address this problem, the integrated navigation method using marine inertial navigation system and star sensor based on model predictive filtering (MPF) is proposed in this paper. The MPF uses predictive output tracking measurement output to correct the system model to achieve star sensor positioning error suppression. The estimated position error is fed back to star sensor to obtain accurate location information. Then, the accurate positioning information is used as the reference, and then fed back to the integrated navigation system to obtain high-precision navigation information. Finally, the method is verified by simulation, and the effectiveness of the method is verified by the real ship test on the lake surface.

Inertial positioning technology is one of the most important positioning techniques. It is suitable for environments without GPS, lack of radio base stations or have no priori geographic information. The pedestrian location system based on Micro Inertial Measurement Unit (MIMU) is usually integrated in the foot of the pedestrian and using the inertial law to solve the pedestrian's position, velocity and attitude information. What's more, the inertial location system utilizes the Zero Velocity Update (ZUPT) algorithm to suppress their errors. However, ZUPTs cannot effectively decrease pedestrian position errors and heading angle error. So pedestrian's position errors and heading angle error will still accumulate over time. Because the magnetometer can detect earth's magnetic field information and solving the heading angle by it, in this paper, a dual MIMU/ magnetometer/ ultrasonic pedestrian location scheme is proposed. The two MIMUs, two magnetometers and two ultrasonic modules are respectively fixed on the left and right foot of pedestrians, and the output information of magnetometer is used to assist the ZUPT algorithm to calculate the locating information, which suppresses positioning error and heading error. We use two ultrasonic modules to measure the spatial relative position relation of pedestrian's left foot and right foot in real time and construct the second order nonlinear constraint equation. We correct pedestrian position information continually by using this constraints so that to improve the positioning accuracy. To verify this program, in this paper, some experiments have been carried out. We have designed different pedestrian trajectories in certain building. After the experiment we processed and analyzed the offline data from this integrated positioning system. The experimental results show that compared with single MIMU pedestrian location scheme, pedestrian positioning technology based on dual MIMU/ magnetometer/ ultrasonic module has higher positioning accuracy, and the positioning accuracy of this system is better in the longer time, which can meet the requirement of pedestrian positioning where there is no external auxiliary information.

In order to solve the problem that the indoor pedestrian navigation cannot receive the GPS signal, the inertial navigation and positioning technology, which does not rely on any external equipment or a priori database, is a more reliable method. The inertial navigation system can calculate the position, velocity and attitude information by collecting the acceleration and angular velocity information. On the basis of it we introduce zero speed correction, when the system detects pedestrians at zero speed state, namely the foot meets the ground, the Kalman filter algorithm is triggered and the state of the system is corrected to eliminate the errors of SINS. In the indoor environment, experiments were carried out for different walking routes. The simulation results show that the positioning accuracy of the navigation algorithm with zero velocity correction is improved obviously.

Sail for a long time in the sea, and the inertial navigation system error accumulating with time affect navigation accuracy seriously, meanwhile the star sensor can output the high precision information. So inertial navigation / Star integrated navigation technology is applied to solve this problem. Through the strap-down inertial navigation system and the star sensor, the position information of the ship is calculated during the navigation process, and the difference of the position information is taken as the observation quantity. Then, the Kalman filter is used to fuse the data information, so as to estimate effectively the integrated navigation system error terms. The purpose is to modify the navigation information of the inertial navigation system. Through the system simulation, it can be seen that this combination navigation method can effectively suppress the divergence of the inertial navigation system error and improve the navigation accuracy when navigating the ship.