1. Measuring the true Integrity of Navigation in Real-Time Antti A. I. Lange PhD www.FKF.net

2. Overview: Integrity of Optimal Kalman filtering Helmert-Wolf blocking (HWB) of Geodesy the Fast Kalman Filtering (FKF) uses HWB Measuring the true integrity by C.R.Rao’s MINQUE with FKF Concluding remarks

4. Integrity of Optimal Kalman Filtering:

5. Fast Kalman processing

9. Minimum-Norm-Quadratic-Unbiased- Estimation (MINQUE) theory: The measuring accuracies of many correlated observations was solved reliably in1970 by C.R.Rao’s MINQUE that optimally exploits internal consistency of the GNSS and other supporting data

11. Concluding remarks: The Fast Kalman Filtering (FKF) using the HWb method extends the precision of Real-Time-Kinematic (RTK) and Virtual- Refence-Station (VRS) surveying to all GNSS engineering and precision navigation applications The real-time precision of the FKF navigation depends crucially on local information density which is a function of both speed of the vehicle and the number of available GNSS signals and frequencies including INS and other signals Ultra-reliable accuracy estimates of the GNSS and other signals including IMU are operationally computable only using the Minimum-Norm-Quadratic-Unbiased-Estimation (MINQUE) methods with the help of the patented FKF (PCT/FI2007/00052)

12. Early warnings of tsunamis, earth quakes, shaking buildings and collapsing bridges etc. become now possible with GPS, Glonass, Galileo, Beidou, IRNSS, DORIS, QZSS, SBAS, GBAS and other positioning methods exploiting all available combinations for absolutely the best possible results Project proposals for expedient implementation of the FKF processing are now welcome for ultra-reliable precision piloting and navigation for all safety-critical ITS applications Please contact directly the inventor of FKF: Mr. Antti A. I. Lange Ph.D., +358400373182 or +35891355450, lange@fkf.net, www.FKF.net, skype: kalmanfilter. Concluding remarks cont'd: