Method and apparatus for locating position of a satellite signal receiver is described. In one example, pseudoranges are obtained that estimate the range of a satellite signal receiver to a plurality of satellites. An absolute time and a position are computed using the pseudoranges at a first time. The absolute time is then used to compute another position at a subsequent time. In another example, a plurality of states associated with a satellite signal receiver are estimated, where the plurality of states includes a time tag error state. A dynamic model is then formed relating the plurality of states, the dynamic model operative to compute position of the satellite signal receiver.

Method and apparatus for processing of satellite signals without time of day information

Method and apparatus for forming a dynamic model to locate position of a satellite receiver

Method and apparatus for receiving an estimate of time in a satellite signal receiver receives an estimate of time from a server and compensates for error of a clock in the satellite signal receiver using the estimate of time. The output of the compensated clock is used when computing a position of the satellite signal receiver. The estimate of time is received using a network time protocol (NTP), a simple network time protocol (SNTP), or by one-way broadcast from the server.

Method and system for determining time in a satellite positioning system

Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.

A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach

A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels (611 - 631) of the wireless environment (RN). Each submodel indicates a probability distribution (F1 - F3) for signal values at one or more locations (Q1 - QY) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations comprises combining the inverse cumulative distribution functions of the submodels or existing locations.

Probabilistic model for a positioning technique

This paper presents an Interacting Multiple Model (IMM) estimator based approach to navigation using the Global Positioning System (GPS). The "soft-switching" IMM estimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matched to different motion modes of the platform e.g., nearly constant velocity and maneuvering. The goal is to obtain the maximum navigation accuracy from an inexpensive and light GPS-based system, without the need for an inertial navigation unit, which would add both cost and weight. In the case of navigation with maneuvering, for example, with accelerations and decelerations, the IMM estimators can substantially improve navigation accuracy during maneuvers as well as during constant velocity motion over a conventional (extended) Kalman Filter (KF), which is, by necessity, a compromise filter. This paper relies on a detailed modeling of GPS and presents the design of a navigation solution using the IMM estimator. Two different IMM estimator designs are presented and a simulated navigation scenario is used for comparison with two baseline KF estimators. Monte Carlo simulations are used to show that the best IMM estimator significantly outperforms the KF with about 40-50% improvement in RMS position, speed and course errors.

http://ieeexplore.ieee.org/iel5/7416/20155/00931509.pdf

Enhanced accuracy GPS navigation using the interacting multiple model estimator

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Enhanced accuracy GPS navigation using the interacting multiple model estimator