A state‑domain robust autonomous integrity monitoring with an extrapolation method for single receiver positioning in the presence of slowly growing fault

Single receiver positioning has been widely used as a standard and standalone positioning technique for about 25 years.To detect the slowly growing faults caused by satellite and receiver clocks in single receiver positioning,the Autonomous Integrity Monitoring with an Extrapolation method(AIME)was proposed based on the Kalman filter measurement domain.However,AIME was designed with the assumption of there is the same number of visible satellites at each epoch,which limits its application.To address this issue,this paper proposes a state-domain Robust Autonomous Integrity Monitoring with the Extrapolation Method(SRAIME).The slowly growing fault detection statistics is established based on the difference between the estimates of the state propagator and the posterior state estimation in Kalman filtering.Meanwhile,singular value decomposition is adopted to factor the covariance matrix of the difference to increase computational robustness.Besides,the relevant formulas of the proposed method are theoretically derived,and it is proven that the proposed method is suitable for any positioning model based on the Kalman filter.Additionally,the results of two experiments indicate that SRAIME can detect slowly growing faults in single receiver positioning earlier than AIME.

Frequency Structures Vibration Identified by an Adaptative Filtering Techiques Applied on GPS L1 Signal

This paper is part of a research under enhancement since 2001, in which the main objective is to measure small dynamic displacements by analysis of L1 GPS carrier frequency with 1575.42 MHz—wavelength 19.05 cm, under an adaptive method for collecting data and filtering techniques. This method, named Phase Residual Method (PRM) is based on the frequency domain analysis of the phase residuals resulted from the L1 double difference static data processing of two satellites in almost orthogonal elevation angle. In this work it is proposed to obtain the phase residuals directly from the raw phase observable collected in a short baseline during a limited time span, in lieu of obtaining the residual data file from regular GPS processing programs. In order to improve the ability to detect millimetric displacements, two filtering techniques are introduced. The first one is the autocorrelation that reduces the phase noise with random time behavior. The other one is the running mean to separate low frequency from the high frequency phase sources. Two trials are presented to verify the proposed method and filtering techniques applied. One simulates a 2.5 millimeter vertical GPS antenna displacement and the second using the data collected during a bridge dynamic load test. The results show a good consistency to detect millimetric oscillations from L1 frequency and filtering techniques.

Passive source range estimation with a single receiver in shallow water

An approach for long-range passive impulsive source ranging with a single receiver in shallow water is proposed, which utilizes the frequency spectrum of the warped signal autocor- relation function via warping transform. For an ideal waveguide, there are invariable frequency features both in the frequency spectrum of the warped signal corresponding to modal cut-off frequencies and the warped signal autocorrelation function due to modal interference. These intrinsic frequency features can be used to passive source ranging. So, the approximate rela- tionship between the frequency of warped signal at an unknown source range and the intrinsic frequency extracted by the time warping transform is derived. These rules can be generalized to an actual shallow water waveguide. Employing an acoustic model to offer the invariable frequency spectrum features, the impulsive signal data collected by a single hydrophone in the North Yellow Sea in December 2011 are analyzed to verify the proposed source ranging ap- proach. The estimated ranges are in good agreement with the ranges measured by GPS, and the mean relative error of range estimation is less than 10%.