Sirf Technology, Inc.

Csr Technology Inc.

Daniel Liao

Mangesh M Chansarkar

Xiangdong Lin

Gengsheng Zhang

Lionel Jacques Garin

Gang Xie

Systems and methods for processing navigational solutions are provided. In this regard, a representative system includes a navigational computing device comprising a processor and memory that stores a navigational solution manager that is executed by the processor to provide a receiver position. The navigational solution manager is configured to collect navigational data from at least one satellite vehicle, compute initialization navigational measurements based on the collected navigational data, and compute the receiver position using the initialization navigation measurements.

Systems and methods for processing navigational solutions

Systems and methods for detecting global positioning system (GPS) measurement errors are provided. In this regard, a representative system, among others, includes a navigation device that is configured to receive GPS signals from signal sources, the navigation device being configured to calculate pseudoranges (PRs) and delta ranges (DRs) based on the received GPS signals, the navigation device including a consistency check algorithm that is configured to: determine mismatches between the respective calculated PRs and DRs, and indicate that an error exists in the respective calculated PRs and DRs based on their mismatch and mismatch accumulations. This algorithm can be independent of navigation state and is capable of detecting slow-changing errors.

Systems and methods for detecting GPS measurement errors

A method for GPS navigation which uses an interacting multiple-model (IMM) estimator with a probabilistic data association filter (PDAF) improves navigation performance. The method includes (a) providing two or more models of GPS navigation, with each model characterized by a model state vector which is updated periodically, (b) providing for each model a corresponding filter for deriving, for each period, a current value for the corresponding model state vector based on current measurements made on parameters affecting the corresponding state vector; and (c) applying an interacting multiple model (IMM) estimator to provide, for each period, a current value for a system state vector using the current values of the model state vectors for that period and their corresponding filters. Each model state vector may include one or more of the following: variables: 3-dimensional position, 3-dimensional velocity, satellite clock bias, satellite clock drifts and one or more other satellite parameters. The current value of the system state vector may be a weighted average of the current values of the model state vectors, where the weights are a set of mode probabilities. In addition, one or more of the filters is a probabilitic data association filter (PDAF).

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Irvine, CA, United States of America

Daniel Liao