GPS/INS Kalman filter error budget
Procurement and analysis teams comparing filter error terms against public GPS/INS reports.
Heritage notes kept in view
Position and Attitude Data System (PADS) The Position and Attitude Data System (PADS) is a high accuracy, near real time, long range airborne direct georeferencing system.
It integrates GPS and inertial sensor measurements using Kalman filtering techniques to precisely determine multiple target locations on the ground.
It consists of an Inertial Measurement Unit (IMU), GPS receiver, and Integration Computer System (ICS) running the Applied Analytics Real Time Navigator (RTN) software package.
Features Hardware/software for precision roll, pitch, heading and navigation Complete inertial navigation solution at IMU data rate (e.g.
Source notes for this comparison
The incorporation of all sensor measurements in one filter with high integration rates provides a much more accurate solution than "separate box" architectures.
In addition, ground and flight testing has shown that the tightly coupled EGI "blended solution" has far superior performance under less than 4 satellite tracking conditions than a stand alone GPS receiver [8].
Since an aircraft or missile is subjected to continuous specific force (due to thrust, lift and drag), the Kalman filter can obtain estimates of sensor errors as maneuvering makes sensor errors visible to the filter through position and velocity errors.
Continuous calibration of the inertial sensors permits an accurate position, velocity and attitude solution during GPS outages (such as jamming).
This also allows manufacturers to use lower cost inertial sensors.
ribes a simple method for the integration of INS and GPS under a restrictive set of assumptions.
The assumptions of a flat nonrotating earth with constant fields brought much simplification to the navigation problem.
The purpose of this document is to offer a simple introduction into the complex field of strapdown inertial navigation systems and GPS/INS integration.
The basic principles covered here may be extended to more complex navigation and filtering problems.
Appendix A In this appendix the Kalman filter used in Algorithm 3 will be derived from B ayes’ theorem [4].
𝑥𝑘 = 𝐴𝑥𝑘−1 + 𝑞𝑘−1 𝑦𝑘 = 𝐻𝑥𝑘 + 𝑟𝑘 Here 𝑥𝑘 is the n-dimension state at time step k, A is the state transition matrix, 𝑦𝑘 is the vector of sensor measurements and H describes the connection between the states and measurements.
ess the GPS SIS tracking data supplied by the MSs and generates estimates for the satellite clock/ephemeris parameters in near real time.
Working method for this table
The preserved notes below keep identity; the table keeps the figures honest. Both have to match the source.
Identity stays with the archived engineering record. Commerce stays off-page: no prices, no SKUs.
Procurement and analysis teams comparing filter error terms against public GPS/INS reports. Verify every figure against the original report before a drawing release.
Public reports in this retrieval set: NASA Space Shuttle navigation GPS IMU; A Short Tutorial on Inertial Navigation System and Global Positioning System Integration; GPS SPS Performance Standard 2020; Integrated INS/GPS Navigation from a Popular Perspective; Space shuttle navigation analysis. Volume 2: Baseline system navigation; Absolute Navigation Performance of the Orion Exploration Fight Test 1.
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This independent educational reference summarizes general technical concepts. Verify current standards, dimensions, and manufacturer specifications before making a procurement or engineering decision.
Numbers the documents actually state
Rows below follow documents opened for “gps ins kalman filter error budget”. If a cell disagrees with the PDF, keep the PDF.
| Document | Stated figure | Source |
|---|---|---|
| NASA Space Shuttle navigation GPS IMU | In addition, ground and flight testing has shown that the tightly coupled EGI "blended solution" has far superior performance under less than 4 satellite tracking conditions than a stand alone GPS receiver [8]. | 20100039589 |
| A Short Tutorial on Inertial Navigation System and Global Positioning System Integration | Appendix A In this appendix the Kalman filter used in Algorithm 3 will be derived from B ayes’ theorem [4]. | 20150018921 |
| GPS SPS Performance Standard 2020 | ess the GPS SIS tracking data supplied by the MSs and generates estimates for the satellite clock/ephemeris parameters in near real time. Because Kalman filters do not react instantaneously to unpredictable changes, and. | performance-standards-specifications |
| Integrated INS/GPS Navigation from a Popular Perspective | asize increased accuracy of INS/GPS versus INS and reliability of navigation, as well as lower size and weight, and higher power, fault tolerance and long life. The principles of GPS are not discussed; rather the. | 20020041935 |
| Space shuttle navigation analysis. Volume 2: Baseline system navigation | 2 when the rss (filter-indicated) position error is greater than 3280 ft and 1. | 19800022935 |
| Absolute Navigation Performance of the Orion Exploration Fight Test 1 | The measurement standard deviations for PR and DR used in the filter are 60 ft and 3 ft, respectively, which are large enough numbers that the inclusion of satellite specific bias states was not necessary. | 20160001440 |
| Orion Exploration Flight Test-l (EFT -1) Absolute Navigation Design | Unique challenges associated with designing the navigation system for EFT-1 are presented in the narrative with an emphasis on how redundancy and robustness influenced the architecture. | 20140011752 |
| Application of GPS to Enable Launch Vehicle Upper Stage Heliocentric Disposal | The altitude limit is especially important to the vehicle’s large radial velocity coming out of the TLI manuever as shown in Figure 5. | 20170012384 |