摘要:
运用位置指纹与行人航位推算(pedestriandeadreckoning,PDR)融合的方法研究室内定 位算法,以提高室内定位精度。 对于位置指纹算法,通过优化指纹数据库完成离线数据训练,通 过限定区域加权 K 实现最优邻近法的在线实时匹配 。 对于行人航位推算法,提出自适应加权波 峰检测算法检测步频,改进了步长估算的非线性模型,融合陀螺仪和磁力计信息进行航向估计。 最终运用无迹卡尔曼滤波器对位置指纹和PDR进行融合,提高了定位精度,并在定位系统中进 行了验证和应用。
Abstract:
To improve the indoor positioning accuracy, the methods of position fingerprint and pedestriandeadreckoning(PDR)areusedtostudytheindoorpositioningalgorithm.Forpositionfingerprintalgorithm,thefingerprintdatabaseisoptimizedthroughtheofflinedatatrainingphase,andthe optimizationofthenearestneighboralgorithmiscarriedoutbylimitingtheregionweightedvalue K throughtheonlinereal-timematchingphase.ForPDRalgorithm,theself-adaptationpeakdetectionalgorithmusedforstepfrequencydetectionisproposed.Theimprovednonlinearmodelisusedforstep sizeestimation.Moreover,Thegyroscope’sinformationarefusedtomagnetometer’sinformationinthe heading estimation.Finally, the unscented Kalman filter is used to fuse the position fingerprint algorithmandPDRmethod,whichimprovesthepositioningaccuracyandthepracticabilityofthefusion algorithmisverifiedbythepositioningsystem.
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