This paper focuses on accuracy and other technology trends for inertial sensors, Global Positioning Systems (GPS), and integrated Inertial Navigation System (INS)/GPS systems, including considerations of interference, that will lead to better than 1 meter accuracy navigation systems of the future. For inertial sensors, trend-setting sensor technologies will be described. A vision of the inertial sensor instrument field and strapdown inertial systems for the future is given. Planned accuracy improvements for GPS are described. The trend towards deep integration of INS/GPS is described, and the synergistic benefits are explored. Some examples of the effects of interference are described, and expected technology trends to improve system robustness are presented.

INS/GPS Technology Trends

Inertial Navigation Systems

A new generalized optimum strapdown algorithm with coning and sculling compensation is presented, in which the position, velocity and attitude updating operations are carried out based on the single-speed structure in which all computations are executed at a single updating rate that is sufficiently high to accurately account for high frequency angular rate and acceleration rectification effects. Different from existing algorithms, the updating rates of the coning and sculling compensations are unrelated with the number of the gyro incremental angle samples and the number of the accelerometer incremental velocity samples. When the output sampling rate of inertial sensors remains constant, this algorithm allows increasing the updating rate of the coning and sculling compensation, yet with more numbers of gyro incremental angle and accelerometer incremental velocity in order to improve the accuracy of system. Then, in order to implement the new strapdown algorithm in a single FPGA chip, the parallelization of the algorithm is designed and its computational complexity is analyzed. The performance of the proposed parallel strapdown algorithm is tested on the Xilinx ISE 12.3 software platform and the FPGA device XC6VLX550T hardware platform on the basis of some fighter data. It is shown that this parallel strapdown algorithm on the FPGA platform can greatly decrease the execution time of algorithm to meet the real-time and high precision requirements of system on the high dynamic environment, relative to the existing implemented on the DSP platform.

Field programmable gate array based parallel strapdown algorithm design for strapdown inertial navigation systems.

Inertial navigation computation is to acquire the attitude, velocity and position information of a moving body by integrating inertial measurements from gyroscopes and accelerometers. Over half a century has witnessed great efforts in coping with the motion non-commutativity errors to accurately compute the navigation information as far as possible, so as not to comprise the quality measurements of inertial sensors. Highly dynamic applications and the forthcoming cold-atom precision inertial navigation systems demand for even more accurate inertial navigation computation. The paper gives birth to an ultimate inertial navigation algorithm to fulfill that demand, named the iNavFIter, which is based on a brand new framework of functional iterative integration and Chebyshev polynomials. Remarkably, the proposed iNavFIter reduces the non-commutativity errors to almost machine precision, namely, the coning/sculling/scrolling errors that have perplexed the navigation community for long. Numerical results are provided to demonstrate its accuracy superiority over the-state-of-the-art inertial navigation algorithms at affordable computation cost.

Next-Generation Inertial Navigation Computation Based on Functional Iteration

This thesis addresses the issue of positioning in 6-DOF that arises from augmented reality applications and focuses on embedded sensors based solutions.Nowadays, the performance reached by visual-inertial navigation systems is starting to be adequate for AR applications. Nonetheless, those systems are based on position correction from visual sensors involved at a relatively high frequency to mitigate the quick drift of low-cost inertial sensors. This is a problem when the visual environment is unfavorable.In parallel, recent works have shown it was feasible to leverage magnetic field to reduce inertial integration drift thanks to a new type of low-cost sensor, which includes – in addition to the accelerometers and gyrometers – a network of magnetometers. Yet, this magnetic approach for dead-reckoning fails if stationarity and non-uniformity hypothesis on the magnetic field are unfulfilled in the vicinity of the sensor.We develop a robust dead-reckoning solution combining simultaneously information from all these sources: magnetic, visual, and inertial sensor. We present several approaches to solve for the fusion problem, using either filtering or non-linear optimization paradigm and we develop an efficient way to use magnetic error term in a classical bundle adjustment that was inspired from already used idea for inertial terms. We evaluate the performance of these estimators on data from real sensors. We demonstrate the benefits of the fusion compared to visual-inertial and magneto-inertial solutions. Finally, we study theoretical properties of the estimators that are linked to invariance theory.

Improving Visual-Inertial Navigation Using Stationary Environmental Magnetic Disturbances

This paper provides an overview of the primary strapdown inertial system computational elements and their interrelationship. Using an aircraft type strapdown inertial navigation system as a representative example, the paper provides differential equations for attitude, velocity, position determination, associated integral solution functions, and representative algorithms for system computer implementation. For the inertial sensor errors, angular rate sensor and accelerometer analytical models are presented including associated compensation algorithms for correction in the system computer. Sensor compensation techniques are discussed for coning, sculling, scrolling computation algorithms and for accelerometer output adjustment for physical size effect separation and anisoinertia error. Navigation error parameters are described and related to errors in the system computed attitude, velocity, position solutions. Differential equations for the navigation error parameters are presented showing error parameter propagation in response to residual inertial sensor errors (following sensor compensation) and to errors in the gravity model used in the system computer. 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. 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 rotating earth. I Frame = "Inertial" non-rotating coordinate frame.

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Paul G. Savage

Papers

Computational Elements for Strapdown Systems