B8.1 - Motion State Prediction by Unaided Inertial Micro-Machined Accelerometers

摘要: Abstract: Estimating motion velocity state is crucial in dealing with inertial navigation systems (INS) or situations where backing of an INS by other technologies difficult impossible. This study therefore investigates time sequences delivered embedded sensors order to draw conclusions about the moving objects. Various probability tests were evaluated a simple but typical measurement setup assess robustness against random walk fluctuations and behavior constant state, detect transition from standstill vice versa. Our investigations end proposal for advanced estimation algorithms, different statistical approaches have been combined. Key words: accelerometer, indoor localization, system, walk, zero-velocity update. 1 Introduction The satellite based global positioning system (GPS) has certainly revolutionized our everyday lives. However, electromagnetic waves are strongly shielded outer shell walls buildings. Even if can penetrate into building, reflection diffraction effects makes localization on wave propagation nearly Hence, there still strong demand systems, e.g. (INS). Due enormous development steps performance, size price during last decade many today microelectromechanical (MEMS). output MEMS sensor typically represents acceleration angular rate object. Consequently, one interested position object, signal generally be integrated twice respect [1]. One most challenging tasks must surely cope types deterministic stochastic error sources [2][3], offset variations, also known as Brownian infiltrating at proof mass drifts molecules [4][5]. Regardless how signals modeled [6][7][8], mastering very task designing positioning/tracking solutions, even only interest. Motion essentially subdivided seemingly reliably detecting (i) standstill, (ii) zero (iii) unfailing detection ongoing motion, (iv) standstill. article will describe various aim determining object equipped strapdown INS. Insight demands unaided positioningis Section 2. In 3 discussed. Measurements characterize their performance presented 4. Combining improves results substantially. demonstrated 5. Concluding remarks given final 6.