the resultant confidence ellipse after n iterations has axes of dimension n u00d7 ab and n u00d7 cd or more simply in the worst case these ellipses grow linearly with travel distance clearly the accuracy of the sensors is critical to the confidence that may be placed in position estimation using dead reckoning unfortunately the sensors on most mobile smart phones are inaccurate and are severely impacted by noise as a result a number of noise filtering algorithms were investigated including kalman filter based dead reckoning and the savitzky golay smoothing filter however none of these seemed suitable for real time performance on mobile phone systems it was finally decided to implement a finite impulse response filter a method proposed by j benjamin gotow et al they recently proved that an adapted fir filter may be used successfully on iphone as well as android phones with acceptable accuracy in addition the more advanced savitzky golay smoothing filter may be applied offline by uploading the raw sensor data to a backend server which may run the data and then provide corrections to algorithm periodically fig2 outlines the basic algorithm for filtering compass heading fig3 shows the results of the filtering algorithm on raw sensor data within an iphone implementation in this accelerometer filter implementation different colors not shown may be used to represent accelerations in different orthogonal axes in the preferred embodiment this technique allows users to record video and tag it with its current location this tag contains additional metadata that is uploaded to a server and is associated with video file the format of the metadata not only contains longitude latitude and heading data but also grid coordinates that are calculated based on the location estimation obtained once the gps coordinates.