Global Positioning Systems, Inertial Navigation, and Integration

Chapter 5: GLOBAL NAVIGATION SATELLITE SYSTEM DATA ERRORS

GLOBAL NAVIGATION SATELLITE SYSTEM DATA ERRORS

5.1 SELECTIVE AVAILABILITY ERRORS

Prior to May 1, 2000, Selective Availability (SA) was a mechanism adopted by the Department of Defense (DOD) to control the achievable navigation accuracy by nonmilitary GPS receivers. In the GPS SPS mode, the SA errors were specified to degrade navigation solution accuracy to 100 m (2D RMS) horizontally and 156 m (RMS) vertically.

In a press release on May 1, 2000, the President of the United States announced the decision to discontinue this intentional degradation of GPS signals available to the public. The decision to discontinue SA was coupled with continuing efforts to upgrade the military utility of systems using GPS and supported by threat assessments that concluded that setting SA to zero would have minimal impact on United States national security. The decision was part of an ongoing effort to make GPS more responsive to civil and commercial users worldwide.

The transition as seen from Colorado Springs, Colorado (USA) at the GPS Support Center is shown in Fig. 5.1. The figure shows the horizontal and vertical errors with SA, and after SA was suspended, midnight GMT (8 p.m. EDT), May 1, 2000. Figure 5.2 shows mean errors with and without SA, with satellite PRN numbers.

Aviation applications will probably be the most visible user group to benefit from the discontinuance of SA. However, precision approach will still require some form of augmentation to ensure that integrity requirements are met. Even though setting SA to zero reduces measurement errors, it does not reduce the need for and design of WAAS and LAAS ground systems and avionics.

Time and frequency users may see greater effects in the long term via communication systems that can realize significant future increases in effective bandwidth use due to tighter synchronization tolerances. The effect on vehicle tracking applications will vary. Tracking in the trucking industry requires accuracy only good enough to locate in which city the truck is, whereas public safety applications can require the precise location of the vehicle. Maritime applications have the potential for significant benefits. The personal navigation consumer will benefit from the availability of simpler and less expensive products, resulting in more extensive use of GPS worldwide.

Because SA could be resumed at any time, for example, in time of military alert, one needs to be aware of how to minimize these errors.

There are at least two mechanisms to implement SA. Mechanisms involve the manipulation of GPS ephemeris data and dithering the satellite clock (carrier frequency). The first is referred to as epsilon-SA (ε-SA), and the second as clock-ditherSA. The clock-dither SA may be implemented by physically dithering the frequency of the GPS signal carrier or by manipulating the satellite clock correction data or both.

Although the mechanisms for implementation of SA and the true SA waveform are classified, a variety of SA models exist in the literature [e.g., [4, 24, 35, 212]]. These references show various models. One proposed by Braasch [24] appears

to be the most promising and suitable. Another used with some success for predicting SA is a Levinson predictor [10].

The Braasch model assumes that all SA waveforms are driven by normal white noise through linear system [autoregressive moving average (ARMA)] models (see Chapter 3 of Ref. 66). Using the standard techniques developed in system and parameter identification theory, it is then possible to determine the structure and parameters of the optimal linear system that best describes the statistical characteristics of SA. The problem of modeling SA is estimating the model of a random process (SA waveform) based on the input/output data.

The technique used to find an SA model involves three basic elements:

The observed SA
A model structure
A criterion for determination of the best model from the set of candidate models

There are three choices of model structures:

  1. An ARMA model of order (p,q), which is represented as ARMA(p,q)
  2. An ARMA model of order (p,0) known as the moving-average MA(p) model
  3. An ARMA model of order (q,0), the auto regression AR(q) model

Selection from these three models is performed with physical laws and past experience.

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