초록 ▼

An Ultra-Tightly Coupled GPS-inertial navigation system for use in a moving agile platform includes a range residual extractor that uses best curve fitting of a third order polynomial for estimating range residual. The curve-fitted residual is used to update an error Kalman filter. The error Kalman

An Ultra-Tightly Coupled GPS-inertial navigation system for use in a moving agile platform includes a range residual extractor that uses best curve fitting of a third order polynomial for estimating range residual. The curve-fitted residual is used to update an error Kalman filter. The error Kalman filter includes correction for navigation solution, and IMU and GPS parameters. The navigation solution together with GPS parameter corrections are used in a Tracking Predictor to generate high-sampling-rate carrier and code replicas. The curve-fitting error covariance indicates signal to noise ratio for the tracked GPS signal and may be used for early indication of interference or jamming.

대표청구항 ▼

What is claimed is: 1. A method of determining a relative range parameter that is descriptive of a relative range (RR) that changes as a function of time, the relative range parameter representing for a first corresponding time point, one or more of changing range aspects between a first physical o

What is claimed is: 1. A method of determining a relative range parameter that is descriptive of a relative range (RR) that changes as a function of time, the relative range parameter representing for a first corresponding time point, one or more of changing range aspects between a first physical object and a second physical object moving relative to each other, said method comprising: (a) receiving a signal at said first object from said second object; (b) obtaining over-time samplings, including past samplings in a neighborhood leading up to the first corresponding time point, of at least one relative phase signal representing a changing phase condition between said received signal and a locking-on replica signal produced by said first object; (c) applying a curve fitting algorithm to said obtained over-time samplings of the at least one relative phase signal, where the curve fitting algorithm produces a fitted and parameterized curve extending at least to the first corresponding time point and extending through the neighborhood of said past samplings; and (d) predictively determining the relative range parameter for the first corresponding time point from use of said curve fitting algorithm; (e) repeating steps (a) to (d) for a next corresponding time point; where said locking-on replica signal used in conjunction with the first corresponding time point is produced in response to a carrying out of predictive step (d) for a previous corresponding time point preceding the first corresponding time point. 2. The method of claim 1 wherein said relative range parameter is selected from the group consisting of: a derivative of a relative range, or a combination of both a relative range and a derivative of said relative range. 3. The method of claim 1 wherein: said first object is an agile platform whose motions tend to induce substantial Doppler shifts in the frequency and phase of the received signal; said second object is a GPS space vehicle; and said signal is a GPS signal comprising at least one pseudo-random code sequence. 4. The method of claim 1 wherein said relative phase is a relative carrier phase between a carrier phase of said signal and a carrier phase of said replica signal. 5. The method of claim 1 wherein said relative phase is a relative code phase between a code phase of said signal and a code phase of said replica signal. 6. The method of claim 1 wherein said relative phase is a combination of a relative carrier phase between a carrier phase of said signal and a carrier phase of said replica signal, and a relative code phase of said signal and a code phase of said replica signal. 7. The method of claim 1 wherein said curve fitting algorithm uses at least one technique selected from the group consisting of: an Nth order polynomial fit, a spline fit, a Fourier series fit, and a Tchebychev fit. 8. The method of claim 7 wherein said curve fitting algorithm is an Nth order polynomial fit applied to m samples of said at least one relative phase and, wherein m is greater than N. 9. The method of claim 8 wherein m is selected for having a matching output sampling rate corresponding to an input rate of a predefined extended Kalman filter. 10. The method of claim 1 wherein said curve fitting algorithm processes said obtained over-time samplings of the at least one relative phase using backward running time. 11. A machine-implemented method for predictively defining a relative range as a function of time between a first physical object and a second physical object moving relative to each other, the machine-implemented method comprising: (a) automatically receiving a signal transmitted from the second object, wherein said received signal comprises a plurality of embedded code streams; (b) obtaining past relative phase samples by automatically measuring over time and storing at least one relative phase indicator indicative of relative phase between said embedded code stream and a corresponding at least one, locking-on replica code steam produced by said first object; (c) automatically applying a curve fitting algorithm to said obtained past relative phase samples so that the curve fitting algorithm produces a fitted and predictive curve which predicts a next expected value and next expected rate of change for the measured relative phase; and (d) automatically using the predicted relative range values to assist in future carrying outs of said automated step of receiving the signal transmitted from the second object. 12. A machine-implemented method for determining relative range as a function of time between first and second physical objects moving relative to one another, the machine-implemented method comprising: (a) receiving at said first object a first signal transmitted from said second object, where said first signal includes a first embedded code stream that can be used to define the phase of said first signal as received at said first object relative to a corresponding first replica code stream produced in said first object; (b) automatically defining the first relative range between the first and second objects as a first, unknown Nth order polynomial of relative phase as a function of time; (c) within the first object, taking m measurements of relative phase between the first embedded code stream and the first replica code stream, where m is greater than N; (d) automatically fitting a first, Nth order polynomial to the taken, m measurements in accordance with a first predefined curve fitting algorithm; and (e) using the first fitted Nth order polynomial to define a first relative range and one or more over time derivatives of the first relative range at a given first time. 13. The machine-implemented method of claim 12 wherein the first given time follows the associated occurrence times of at least m-1 of the measured relative phases to thereby cause the defining in step (e) of the first relative range and one or more of its over time derivatives to substantially be a predictive definition of the first relative range and its one or more derivatives.