Electronic Version

Carrier phase measurements are extremely accurate but ambiguous. The estimation of the integer ambiguities is in general split in two parts: A least-squares float solu-tion, which is obtained by disregarding the integer prop-erty, and the actual fixing. The latter one can be a simple rounding, a sequential fixing (bootstrapping), or an integer least-squares estimation, which typically includes an integer decorrelation and a search. All these fixing methods suffer from a poor accuracy of the float solution due to the small carrier wavelengths. Moreover, the optimal integer least-squares estimation techniques are extremely sensitive to unknown biases.This paper provides a new group of multi-frequency linear combinations to overcome the previous shortcom-ings: The combinations include both code and carrier phase measurements, and allow an arbitrary scaling of the geometry, an arbitrary scaling of the ionospheric delay, and any preferred wavelength. The maximization of the ambiguity discrimination results in combinations with a wavelength of several meters and a noise level of a few centimetres. These combinations are recom-mended for any application where reliability is more important than accuracy. This paper restricts to linear combinations for Galileo although the concept can be equally applied for GPS or any other GNSS system. Moreover, the paper provides an efficient method for the computation of the success rate of rounding.

This paper proposes a new concept of real-time improvement of atmospheric mass density models (AMDM) using space tracking data aiming at better orbit prediction accuracy for low latitude earth-orbiting (LEO) space objects. Preliminary experiments using CHAMP GPS-derived precise orbit solution data have demonstrated extremely encouraging and promising results in the error reductions of orbit prediction for 3 days. This suggests that an order of error reduction is achievable by proper fine-tuning of the algorithms.

In October 2010, Geoscience Australia released the latest beta version of AUSGeoid09 (beta 0.7), providing an improved geoid model to relate GNSS-derived ellipsoidal heights to the Australian Height Datum (AHD71) and vice versa. This paper quantifies the expected improvement of replacing the current geoid model, AUSGeoid98, with AUSGeoid09 in New South Wales (NSW). Four tests were performed to investigate how well the two geoid models fit known AHD71 heights, based on (1) about 500 AUSPOS solutions, (2) 38 CORSnet-NSW sites, (3) several GNSS-based adjustments, and (4) numerous height control points from these adjustments. It was found that AUSGeoid09 provides a considerably improved fit to AHD71 for GNSS-based height transfer in NSW. The first two tests showed the root mean square (RMS) of residuals improved by factors of 2.7 and 4.1 respectively. The magnitude of N values in NSW will change by up to 0.5 m when AUSGeoid09 is introduced. The adjustment tests confirmed these findings, evidenced by improved variance factors and reduced numbers of flagged residuals. The adjusted height observations showed an improved RMS of the residuals, generally by a factor of about 1.5, but reaching 4.6. In most cases the RMS of the AUSGeoid09-derived height results falls within the expected ±0.05 m accuracy stated by Geoscience Australia.

Fingerprinting is a common technique for indoor positioning using short range Radio Frequency (RF) technologies such as Wireless Location Area Network (WLAN) and Bluetooth (BT). It works in two phases: The first phase is a data training phase in which a radio map for the targeted area is generated in advance, while the second phase is the real-time location determination phase using the radio map. Considering the work amount for generating the radio map, only a few samples of the Radio Signal Strength Indicator (RSSI) are typically collected at each reference point. The limited samples are not able to represent the real signal distribution well in the conventional fingerprint approach such as in an occurrence-based solution. This paper presents a new solution using the Weibull function for approximating the Bluetooth signal strength distribution in the data training phase. This approach requires only a few RSSI samples to estimate the parameters of the Weibull distribution. Compared to the occurrence-based solution, the Weibull function utilizes the shape, shift, and scale parameters to describe the distribution over the entire RSSI domain. This study indicates that the reliability and accuracy of the fingerprint database is improved with the Weibull function approach. A Histogram Maximum Likelihood position estimation based on Bayesian theory is utilized in the positioning phase. The test results show that the fingerprinting solution using the Weibull probability distribution performs better than the occurrence-based fingerprint approach.

Locata is a terrestrial position system that offers cm-level accuracies. Acquisition of degraded quality signals has always been a challenging task for radio navigation receivers and Locata is no exception. Locata's use of pseudorandomly gated CDMA signals further exacerbates the situation. This paper proposes four algorithms that offer improved signal acquisition in challenging situations and evaluates the performance of these algorithms in detail using real Locata signals. First, appreciating the complexity involved in the non-coherent acquisition of Locata signals, an algorithm is presented that exploits the inherent characteristics of the Locata gating sequence and offers receiver sensitivity improvement of around 1.3dB each time the integration duration is doubled. A concept of assisted acquisition is then introduced. It is shown that acquisition of any one signal can assist acquisition of the rest allowing reduction in mean acquisition time (MAT) and computational load and offering a further improvement of 1.7dB over previous algorithms. Next the use of long replica codes is suggested so as to allow for coherent integration. It is shown that this offers comparable sensitivity improvement and doesn't require any assistance. Finally an integrated scheme is described that employs the above-mentioned algorithms, and offers a signal acquisition approach better than the conventional one.

This paper presents experimental results of detecting multipath using a recently proposed parameter, early late phase (ELP). The paper shows that positioning error caused by multipath can be reduced by excluding the satellites detected as experiencing multipath from the navigation solution, except where the exclusion causes a significant increase in dilution of precision. Performance of this ELP-based satellite exclusion was compared with standard wide correlators, along with narrow and double delta correlators. It was found that narrow and double delta correlators may in fact increase error when a reflected signal is stronger than the LOS (a scenario possible where the LOS is attenuated) or when multiple reflections are received from the same satellite. In these cases, ELP can still detect multipath and satellite exclusion can mitigate multipath-induced error. Even in more usual cases, satellite exclusion was shown to outperform narrow correlators and performed as well as double delta correlators. However, the exception to this is when there is another satellite affected by multipath not detected using ELP and hence not removed from the solution. In this case, the error may in fact increase by removing one multipath-affected satellite because the multipath biases may be partially cancelling each other.

One of the research focuses in dealing with integer least squares problem is the decorrelation technique to improve the efficiency of the integer parameter search progress. It remains a challenging issue and becomes even more critical in processing multi-GNSS signals. Currently, there are three main decorrelation techniques being employed: the integer Gaussian decorrelation, the Lenstra–Lenstra–Lovász (LLL) algorithm and the inverse integer Cholesky decorrelation (IICD) method. To measure the performance of decorrelation techniques, the condition number is usually used as the criterion. Additionally, the number of grid points in the search space can be directly utilized as a performance measure according to the decorrelation purpose. The success rate of integer bootstrapping is also calculated in terms of studying the ambiguity resolution reliability.This paper presents a modified inverse integer Cholesky decorrelation (MIICD) method to improve the decorrelation performance out the other three techniques. Decorrelation performance is evaluated based on the condition number of the decorrelation matrix and the number of search candidates. Performance parameters are compared using both simulation and real data. The simulation experiment scenarios employ the isotropic probabilistic model using a predefined eigenvalue and without any geometry or weighting system constraints. Simulation analysis shows that MIICD method outperforms other three methods in terms of condition numbers achieved. The real data experiment scenarios involve both single and dual constellations cases. Experimental results demonstrate that in the single constellation case, the condition number of MIICD is smaller than that of LAMBDA over 78.65% times while the number of search candidate points is smaller over 98.92% of time. In the dual constellation case, these two numbers are 98.78% and 100% respectively.