Integer ambiguity resolution in GPS using particle filtering

Azimi-Sadjadi, Babak; Krishnaprasad, P. S.

doi:10.1109/acc.2001.946221

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…From the collected information we generated the pseudo range and the carrier phase data for one static and one moving receiver (base and rover, respectively). We assume that for the carrier phase measurement the integer ambiguity problem is already solved (see Azimi-Sadjadi & Krishnaprasad (2001a) for details). The movement of the INS/GPS platform was simulation based and it was the basis for the measurement data measured by the accelerometers, the gyros, the GPS pseudo range, and the GPS carrier phase data.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Approximate nonlinear filtering and its application in navigation

Azimi-Sadjadi

Krishnaprasad

2005

Automatica

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In this paper, we introduce for the first time particle filtering for an exponential family of densities. We prove that under certain conditions the approximated conditional density of the state converges to the true conditional density. In the realistic setting where the conditional density does not lie in an exponential family but stays close to it, we show that under certain assumptions the error of the estimate given by an approximate nonlinear filter (which we call the projection particle filter), is bounded. We use projection particle filtering in state estimation for a combination of inertial navigation system (INS) and global positioning system (GPS), referred to as integrated INS/GPS. We illustrate via numerical experiments that projection particle filtering outperforms regular particle filtering in navigation performance, and extended Kalman filter as well when satellite loss-of-lock occurs.

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Section: Simulation and Resultsmentioning

confidence: 99%

Approximate nonlinear filtering and its application in navigation

Azimi-Sadjadi

Krishnaprasad

2005

Automatica

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“…These algorithms all lack a proof of convergence. More theoretically involved algorithms that treat the integer ambiguity as a random integer vector can be found in [4] and [5]. Currently, real-time results are not available for these algorithms.…”

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confidence: 99%

A High-Integrity and Efficient GPS Integer Ambiguity Resolution Method

et al. 2003

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An efficient method for obtaining the admissible integer ambiguity hypotheses within a probabilistic volume is combined with an integer hypothesis testing method to reduce the convergence time to the GPS carrier‐phase integers with high probability. The baseline coordinates, float ambiguities, and their covariance matrices are first estimated using a Kalman filter. To reduce the hypothesis set, the covariance matrix decorrelation method, which is the major contribution of the least‐squares ambiguity decorrelation adjustment (LAMBDA) method, is then applied. The reduced admissible hypotheses are tested by the multiple‐hypothesis Wald sequential probability test (MHWSPT) to select, with a given probability, the most likely hypothesis. This method considerably reduces the computational time needed to solve the integer ambiguity resolution problem at each epoch.

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“…Based on certain search criterion [19], the search algorithm can utilize the traditional techniques of mathematical programming to guide the global optimization [20,12,131 and/or decorrelation techniques to reduce the search space [9,16,14]. Guided random searching techniques can be used to combat nonlinearity [21,17]. Note, however, that decorrelation would help speed up searching for the integer solution only if the dimension is not too large [14].…”

Section: Introductionmentioning

confidence: 99%

“…This technique uses only the fractional value of the instantaneous carrier-phase measurement and, hence, the ambiguity function values are not affected by the whole cycle change of the carrier phase or by cycle slips (also see [2,4]). The third category comprises the most abundant group of techniques which are based on the theory of integer least squares [5,6,7,8,9,10,11,12,13,14,15,16,17,18]. Parameter estimation in theory is carried out in three steps: float solution, integer-ambiguity estimation, and fixed solution.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Filtering and Carrier-Phase Ambiguity Resolution for GPS-Based TSPI Systems in Jamming Environment

Lai¹

2007

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Integer ambiguity resolution in GPS using particle filtering

Cited by 14 publications

References 11 publications

Approximate nonlinear filtering and its application in navigation

Approximate nonlinear filtering and its application in navigation

A High-Integrity and Efficient GPS Integer Ambiguity Resolution Method

Time-Frequency Filtering and Carrier-Phase Ambiguity Resolution for GPS-Based TSPI Systems in Jamming Environment

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