SIRU development. Volume 1: System development

A complete description of the development and initial evaluation of the Strapdown Inertial Reference Unit (SIRU) system is reported. System development documents the system mechanization with the analytic formulation for fault detection and isolation processing structure; the hardware redundancy design and the individual modularity features; the computational structure and facilities; and the initial subsystem evaluation results

Strapdown calibration and alignment study. Volume 1 - Development document Final report

Calibration and alignment techniques for inertial sensing uni

Flight test results of the strapdown hexad inertial reference unit (SIRU). Volume 2: Test report

Results of flight tests of the Strapdown Inertial Reference Unit (SIRU) navigation system are presented. The fault tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance. Performance shortcomings are analyzed

Initial Self-Alignment for Marine Rotary SINS Using Novel Adaptive Kalman Filter

The accurate initial attitude is essential to affect the navigation result of Rotary Strapdown Inertial Navigation System (SINS), which is usually calculated by initial alignment. But marine mooring Rotary SINS has to withstand dynamic disturbance, such as the interference angular velocities and accelerations caused by surge and sway. In order to overcome the limit of dynamic disturbance under the marine mooring condition, an alignment method using novel adaptive Kalman filter for marine mooring Rotary SINS is developed in this paper. This alignment method using the gravity in the inertial frame as a reference is discussed to deal with the lineal and angular disturbances. Secondly, the system error model for fine alignment in the inertial frame as a reference is established. Thirdly, PWCS and SVD are used to analyze the observability of the system error model for fine alignment. Finally, a novel adaptive Kalman filter with measurement residual to estimate measurement noise variance is designed. The simulation results demonstrate that the proposed method can achieve better accuracy and stability for marine Rotary SINS

Parameter Identification Method for SINS Initial Alignment under Inertial Frame

The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. The conventional alignment method with parameter identification has been already applied widely, but it needs to calculate the gyroscope drifts through two-position method; then the time of initial alignment is greatly prolonged. For this issue, a novel self-alignment algorithm by parameter identification method under inertial frame for SINS is proposed in this paper. Firstly, this coarse alignment method using the gravity in the inertial frame as a reference is discussed to overcome the limit of dynamic disturbance on a rocking base and fulfill the requirement for the fine alignment. Secondly, the fine alignment method by parameter identification under inertial frame is formulated. The theoretical analysis results show that the fine alignment model is fully self-aligned with no external reference information and the gyrodrifts can be estimated in real time. The simulation results demonstrate that the proposed method can achieve rapid and highly accurate initial alignment for SINS

Performance Analysis Of Strapdown Systems

ABSTRACT This paper provides an overview of assorted analysis techniques associated with strapdown inertial navigation systems. The process of strapdown system algorithm validation is discussed. Closed-form analytical simulator drivers are described that can be used to exercise/validate various strapdown algorithm groups. Analytical methods are presented for analyzing the accuracy of strapdown attitude, velocity and position integration algorithms (including position algorithm folding effects) as a function of algorithm repetition rate and system vibration inputs. Included is a description of a simplified analytical model that can be used to translate system vibrations into inertial sensor inputs as a function of sensor assembly mounting imbalances. Strapdown system static drift and rotation test procedures/equations are described for determining strapdown sensor calibration coefficients. The paper overviews Kalman filter design and covariance analysis techniques and describes a general procedure for validating aided strapdown system Kalman filter configurations. Finally, the paper discusses the general process of system integration testing to verify that system functional operations are performed properly and accurately by all hardware, software and interface elements. COORDINATE FRAMES As used in this paper, a coordinate frame is an analytical abstraction defined by three mutually perpendicular unit vectors. A coordinate frame can be visualized as a set of three perpendicular lines (axes) passing through a common point (origin) with the unit vectors emanating from the origin along the axes. In this paper, the physical position of each coordinate frame's origin is arbitrary. The principal coordinate frames utilized are the following: B Frame = "Body" coordinate frame parallel to strapdown inertial sensor axes. 1 N Frame = "Navigation" coordinate frame having Z axis parallel to the upward vertical at the local position location. A "wander azimuth" N Frame has the horizontal X, Y axes rotating relative to non-rotating inertial space at the local vertical component of earth's rate about the Z axis. A "free azimuth" N Frame would have zero inertial rotation rate of the X, Y axes around the Z axis. A "geographic" N Frame would have the X, Y axes rotated around Z to maintain the Y axis parallel to local true north. E Frame = "Earth" referenced coordinate frame with fixed angular geometry relative to the earth. I Frame = "Inertial" non-rotating coordinate frame. NOTATION V = Vector without specific coordinate frame designation. A vector is a parameter that has length and direction. The vectors used in the paper are classified as "free vectors", hence, have no preferred location in coordinate frames in which they are analytically described

Preliminary design of a redundant strapped down inertial navigation unit using two-degree-of-freedom tuned-gimbal gyroscopes

This redundant strapdown INS preliminary design study demonstrates the practicality of a skewed sensor system configuration by means of: (1) devising a practical system mechanization utilizing proven strapdown instruments, (2) thoroughly analyzing the skewed sensor redundancy management concept to determine optimum geometry, data processing requirements, and realistic reliability estimates, and (3) implementing the redundant computers into a low-cost, maintainable configuration

Inertial gyroscope system application considerations

Criteria for designing inertial gyroscope system

Strapdown calibration and alignment study. Volume 2 - Procedural and parametric trade-off analyses document Final report

Parametric and procedural tradeoffs for alignment and calibration of inertial sensing uni