The key issue in precise positioning using the GPS carrier phase is to solve for the integer ambiguities quickly and correctly. For some navigation applications, external sensors am available that provide auxiliary measurements. For example, in the control and guidance of land vehicles relative to a desired trajectory (e.g., lane-keeping on a highway) the altitude of the roadway as a function of arclength can be accurately curve fit and the lateral distance from the lane center may be measurable by other sensors. These auxiliary sensors can be used to aid and facilitate the GPS integer ambiguity resolution problem. Use of such auxiliary sensors offers the potential to obtain the correct integers when few satellites are available. This paper describes a fast and efficient technique for integer ambiguity resolution when auxiliary sensors are available. The paper will present the theoretical approach and results of experimental tests of the method. Preliminary experimental results (based on 11999 epochs) show that, while the GPS-only integer resolution success rate with 5 satellites is 18%, the altitude aided GPS Integer resolution finds the correct Integers with a 98% success rate. Using the declaration decision parameters |res12| < 0.027, |res1w| < 0.095, |res1n| < 0.012, the correct integers were accepted 87% of the 11999 epochs with no erroneous integers.
Data fusion is the process of combining sensory information from different sources into one representational data format. The source of information may come from different sensors that provide information about completely different aspects of the system and its environment; or that provide information about the same aspect of the system and its environment, but with different signal quality or frequency. Data fusion is a large research area with various applications and methods. In addition to having a thorough understanding of various data fusion methods, it is useful to understand which methods most appropriately fit the corresponding aspects of a particular application. For planning, guidance, and control applications it is critical that the state of the vehicle be accurately estimated. For these applications, the state vector of the vehicle includes the three-dimensional position, velocity, and attitude. Because the state of most physical systems evolve in continuous time, continuous-time dynamic models are of interest.
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