Communications: directive radio wave systems and devices (e.g. – Directive – Including a satellite
Reexamination Certificate
2001-03-23
2002-07-09
Tarcza, Thomas H. (Department: 3662)
Communications: directive radio wave systems and devices (e.g.,
Directive
Including a satellite
Reexamination Certificate
active
06417802
ABSTRACT:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT
(Not applicable)
BACKGROUND OF the INVENTION
This invention relates generally to integrated inertial/GPS navigation systems and more specifically to integrated inertia/GPS navigation systems that achieve improved operational reliability through the use of a plurality of Kalman filters.
The Autonomous Integrity Monitored Extrapolation (AIME™) algorithm is an improved algorithm for integrating the Global Positioning System (GPS) with an inertial navigation system (INS) in a way that the navigation solution has a minimum susceptibility to equipment failures, interference, and jamming. The principal problem with using Kalman filters to integrate GPS with INS is that a slow GPS failure due to interference or spoofing can contaminate the integrated solution before it is detected. The AIME™ algorithm solves this problem by analyzing the residuals of the filter over long time intervals before the corrections are added externally to the INS solution. Because the solution is based on the continuous INS output, it is extremely reliable, and continuous in case of complete loss of the GPS. By using parallel solutions which are not sensitive to the failure, it rejects the contamination and continues using an uncontaminated, reliable precision navigation solution.
This principle is applied in the present invention to carrier tracking. The basis of the invention is the Anti-Jam AIME™ (AJ-AIME™) algorithm. This algorithm achieves at least 20 dB J/S improvement, and possibly 30-40 dB J/S improvement. This is in addition to the J/S improvements from other techniques. Previous solutions to improve J/S performance are based on deep coupling using a vector solution combining both carrier and code measurements and INS errors. These errors are estimated in a large, complex maximum likelihood estimator. One problem with these approaches is that the signals are no longer independent so that receiver autonomous integrity monitoring (RAIM) cannot be used to determine integrity.
The AJ-AIME™ approach avoids the loss of integrity and possible contamination inherent in such vector approaches. Even if jamming is successful, the AIME™ solution provides maximum accuracy for coasting with the calibrated INS solution, since this solution is uncontaminated from the period when jamming or interference first began.
For manned aircraft or long range missiles already equipped with a high-quality INS, the AIME™ algorithm can be implemented in a separate, very inexpensive small box, which adds corrections to the INS solution. For inexpensive short range missiles, or munitions, without INS, the AIME™ algorithm is combined with MEMS technology. This solution uses 1 deg/hr silicon gyros, 20 micro-g accelerometers, and a GPS receiver, all integrated in an inexpensive, small box.
Without WAAS, the failure detection and exclusion (FDE) availability of RAIM for non-precision approach (NPA) using 24-satellite constellations with up to three outages is less than 50%. Using WAAS for Precision Approach CAT I (DH=200 feet), the FDE availability of RAIM is less than 2%. One objective of this invention is to improve FDE availability by up to five orders of magnitude. RAIM is basically a “snapshot” approach using instantaneous noisy position data. Instead of RAIM, this invention uses an integrated systems approach. With today's system technology, including micro-electro-mechanical system (MEMS) technology, and using Kalman filter algorithms, it is possible with this approach to use feedback from rate, acceleration, acceleration rate, etc., up to at least the fourth derivative of position, in the Kalman filter error model.
In order to reject errors caused by satellite clock failures, multipath, excessive atmospheric errors, or subsystem failures, these errors and all other errors are modeled in a bank of Kalman filters in the AIME™ algorithm. Each of these filters is tuned for excessive errors in one channel, or in one group of channels, or in a subsystem. The filter which models the errors correctly will have uncorrelated residuals, because of the “innovations property” of Kalman filters. By estimating the mean of the residuals over many Kalman filter cycles, a test statistic is obtained for each filter. This statistic is the mean-square estimated residual for that filter. The filter with the correct model will have the lowest test statistic and the output of that filter will be used. The other filters have large mean-square estimated residuals, since they do not correctly model the excessive error.
The word “failure” is used herein to represent any excessive error which can contaminate the solution, whether due to an actual subsystem soft failure or due to a temporary excessive instrument error or signal error.
Recursive algorithms are used in order that the estimate can use very large numbers of Kalman filter cycles to detect slow failures. In present civil applications, these estimates use residuals over the past two hour period to detect and exclude errors due to slow satellite clock drifts. This algorithm has been certified for Primary Means and has been flown on commercial airliners for many years. In addition, many years of data have been collected and analyzed from flights in normal airline operation.
To improve anti-jam performance, other approaches have claimed that 30-40 dB J/S improvement can be achieved by a vector processing algorithm using deep coupling, where the INS is integrated with the measurements from both the carrier and code discriminators. This would be done in a large, complex, maximum-likelihood estimator which attempts to estimate all error sources including INS errors. This approach creates an integrity problem since the different satellite measurements are no longer independent, and RAIM-type integrity checking is no longer valid.
There are three additional difficulties with this approach: (1) it does not consider the fundamental problem that arises when integrating inertial information with carrier phase information, (2) it does not properly consider the unique characteristics of inertial system errors, and (3) it does not consider the very different characteristics of carrier phase errors vs. code errors.
The fundamental problem referred to in (1) above is that the wavelength of the carrier is only 19 centimeters. During turbulence or high dynamics, the separation between the INS and the GPS antenna makes it difficult to process changing inertial attitude information fast enough to aid the carrier tracking at the antenna to centimeter accuracy at 1000 Hz without excessive time lags.
The unique characteristics of inertial systems referred to in (2) above arise because inertial errors vary slowly. Gyro and accelerometer errors change at only a fraction of a micro-g per second. This causes phase errors of only a few micrometers per second. This is too small to estimate with short term phase measurements, which are noisy. It is much better to estimate these errors over long periods of time using PR measurements, which have bounded position errors over these long periods.
The difficulty identified in (3) above arises because pseudorange measurements are made at 1 Hz with an accuracy of meters while the carrier phase is measured at 1000 Hz with an accuracy of a centimeter or less. It is not practical to combine these measurements in the same Kalman filter. If they are combined, it is almost impossible to prevent contamination from individual signals.
The solution to (1) above (i.e. aiding the carrier discriminators in turbulence and dynamics) is to use strapdown measurements in body axes at 400 to 1600 Hz to compute the antenna position relative to the INS. These measurements are used to estimate antenna position a short time in the future at 1000 Hz. The short prediction time is adjusted to cancel time lags in the system. This effectively eliminates the dynamic stress that the carrier loop has to track.
The solution to (2) above (i.e. inertial characteristics) is to use only pseudorange information in a navigation Kalman filter wi
Litton Systems Inc.
Malm Robert E.
Mull Fred H.
Tarcza Thomas H.
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