| The individual-soldier indoor navigation system can constantly provide accurate positioning results for the soldiers who are on mission in the blocked or shelter environment. Generally, GNSSs(Global Navigation Satellite Systems) are always incapable of application due to signals severely attenuation, fading and interference inside buildings. While, the methods based on Ultrasonic, UWB(Ultrawideband), WLAN, RFID(Radio Frequency IDentification), which require some additional infrastructures to be carried by users, remain the problem of multipath effects. It is costly, and cannot satisfy the need of uncertain battlefield. Using self-contained sensors to build pedestrian navigation systems has gradually attracted many scholars’ attentions and interests. There are many advantages to make it become a preferred consideration, such as anti-interference, strong active, low cost, small size, and continuous navigation results.In this dissertation, a navigation algorithm which suiting soldiers’ indoor mission based on MIMU(Micro Inertial Measurement Unit) is proposed. The main research and achievements are including six aspects:(1). Combined the variety characteristics of soldiers’ actions and tactics in manoeuver, the optimization location MIMU deployed is considered and a Shoe-Mounted Inertial Navigation System is designed. After analyzed gait cycle and gait phases when walking,several key issues— step detection, step length and heading estimation, position reckoning, and so forth—are put forward.(2). Three step detection algorithms based on acceleration peak, stance phase and self-correlation are realized separately in uniform walking. In view of the problem that people usually move with variable gaits and the three algorithms could not get an ideal result in the situation. a novel stance phase detection is proposed, which is compatible with multi-gait. Then, an algorithm which combining step and stance phase is organized, and it adapts to variable gaits easily, even running. The experiments demonstrate that it shows a higher accuracy than traditions.(3). Two models are introduced to estimate step length. Relying on fitting curves and Least-Squares identification by a group of test data, the value of the parameters in models are computed. Simultaneously, the parameters can be adapted to suit different pace by getting the relation that output of sensors versus preferences. In order to improve stability and reliability when navigating, a decision method, involving step detection and step length estimation, fusing INS+ZUPT(Zero Velocity Updates) and SHS(Step-and-Heading System), is designed, to restrain position divergence during ZUPT losing effectiveness.(4). An Extended Kalman Filter(EKF), based error states, has been designed. Combined with Zero Velocity observations, the filter is used to correct attitude and velocity that directly computed by integral. In addition, the paper gives out the methods of static bias compensation of original data and alignment of horizontal attitude.(5). For the reason that the inevitable heading drift will lead to a worse result than others, a heading error calibration algorithm is built. The algorithm which depends on the stability and convergence characteristic of magnetometers in weak magnetic disturbances, and the information of buildings heading, can correct moving headings effectively.(6). The paper list full algorithm procedures of calibration and implementation. In the end, the algorithm has been verified by indoor navigation experiments. The results indicate that the positioning accuracy can satisfy the need of soldiers’ mission in building, and the RMS is less than 2%. |
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