Data processing: vehicles – navigation – and relative location – Navigation – Employing position determining equipment
Reexamination Certificate
2000-06-13
2001-07-31
Camby, Richard M. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Navigation
Employing position determining equipment
C701S220000, C701S221000
Reexamination Certificate
active
06269306
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to a system and a method for estimating sensor errors and more particularly, to a system and a method which accurately estimates errors within an integrated navigation system, such as scale factor and bias errors.
BACKGROUND OF THE INVENTION
Integrated navigation systems (“INS”) are employed within vehicles in order to provide vehicle position and velocity information with respect to a specified reference frame. A typical INS determines estimates for the position and velocity of a vehicle based on a collection of data taken from inertial sensors such as acceleration and rate sensors mounted in the vehicle, as well as sensors based outside the vehicle such as a global position system (“GPS”). Typically, the INS will use this sensor information, along with a model of vehicle motion behavior to form a set of navigation equations, in order to estimate vehicle position information and derivatives. Conventional INS may be used in “turn-by-turn navigation” systems, within “in vehicle dynamics” systems, and within proposed vehicle enhancements such as “adaptive cruise control.”
A key element and/or function of the INS is the estimation of sensor errors used in the navigation equations. All sensors used by the INS have a scale factor that relates the sensor output to the sensed attribute, and a bias error (i.e., the sensor has a nonzero output even when the sensed attribute is zero). If the bias error or scale factor estimates are calculated incorrectly, the calculated vehicle position, heading and/or speed will be in error, and the reliability of the INS will be undesirably reduced. This sensor error estimation is especially important in situations where data from the GPS may become unavailable (e.g., under bridges or within tunnels).
Efforts have been made to reduce the impact of the scale factor and bias error estimation through the use of high quality inertial measurement equipment. However, the relatively high cost of such equipment is prohibitive for automotive applications. Hence, sensor error estimation is critical in conventional systems utilizing lower quality sensors.
Conventional sensor error estimation is typically performed by developing sensor error models, and then implementing the model parameter estimation as augmented equations in the overall set of navigation equations, usually in a “Kalman” filter. The Kalman filter approach has desirable stability properties, but is somewhat limited, as it provides only statistical representations of errors, and requires the implementation to be added to the overall navigation equations. Other attempts at estimating these types of sensor errors have been made using neural networks. While neural networks have the advantage of learning in the presence of noise, they often require a relatively large number of learning examples (e.g., a training set) which are needed for the training phase. The required “training” process is relatively complicated, time consuming and computationally intensive, and is therefore not suited to be carried out “on-line” or during the normal use of a vehicle.
Applicants' invention addresses these drawbacks and provides a method for accurately estimating sensor errors within an integrated navigation system such as scale factor and sensor bias errors.
SUMMARY OF THE INVENTION
It is a first object of the invention to provide a system and a method for estimating sensor errors which overcomes at least some of the previously delineated drawbacks of prior systems, devices, and/or methods.
It is a second object of the invention to provide a system and a method for estimating sensor errors which is adapted for use within an integrated navigation system.
It is a third object of the invention to provide a system and a method for estimating sensor errors by integrating an INS and a GPS using a linear neuron. The linear neuron adaptively estimates scale factor and bias errors in a yaw rate sensor during the availability of the GPS, and then uses these estimated values to aid the INS during periods of time in which the GPS is unavailable or unsuitable for use. The linear neuron used by the present system and method avoids parameter identification disadvantages and extensive training requirements inherent in neural networks. The present system and method further reduce problems associated with persistent excitation inherent in prior “Kalman” filter type estimations.
According to one aspect of the present invention, a system is provided for estimating errors in a first sensor. The system includes a module which is communicatively coupled to the first sensor and to a second sensor, and which includes a first portion which selectively generates a first value based upon a signal received from the first sensor, a second portion which selectively generates a second value based upon a signal from the second sensor, and a third portion which combines the first and second value, effective to generate a third value. The system further includes a linear neuron which is communicatively coupled to the module and which receives the first value and the third value, and which estimates the errors within the first sensor based upon the received first and third values.
According to a second aspect of the present invention, a system is provided for estimating a scale factor and bias error of a yaw rate sensor which is used within an integrated navigation system having a global position system sensor. The system includes a first portion which receives a first signal from the global position system sensor and calculates a global position system heading angle based upon the first signal; a second portion which receives an integrated navigation system heading angle; a third portion which combines the global position system heading angle and the integrated navigation system heading angle, effective to generate a combined heading angle; and a linear neuron which is communicatively coupled to the third portion, which receives the integrated navigation system heading angle and the combined heading angle and which is effective to accurately estimate the scale factor and the bias error based upon the received integrated navigation system heading angle and combined heading angle.
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Ibrahim Faroog
Pilutti Thomas Edward
Camby Richard M.
Ford Global Tech.
Ford Global Tech.
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