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Geometric Dilution of Precision (DOP)

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Geometric Dilution of Precision (GDOP)

GPS devices calculate your position using a technique called “3-D multilateration,” which is the process of figuring out where several spheres intersect. In the case of GPS, each sphere has a satellite at its center; the radius of the sphere is the calculated distance from the satellite to the GPS device. Ideally, these spheres would intersect at exactly one point, causing there to be only one possible solution to the current location, but in reality, the intersection forms more of an oddly-shaped area. The device could be located within any point in the area, forcing devices to choose from many possibilities. Figure 2-1 shows such an area created from three satellites (using part one’s $GPGSV sentence). The current location could be any point within the gray-colored area. Precision is said to be “diluted” when the area grows larger, which leads to this article’s focus: dilution of precision. The monitoring and control of dilution of precision (or DOP for short) is the key to writing high-precision applications.

Figure 2-1: GPS devices must choose one of several possible solutions to the current location.

DOP values are reported in three types of measurements: horizontal, vertical, and mean. Horizontal DOP (or HDOP) measures DOP as it relates to latitude and longitude. Vertical DOP (or VDOP) measures precision as it relates to altitude. Mean DOP, also known as Position DOP (PDOP), gives an overall rating of precision for latitude, longitude and altitude. Each DOP value is reported as a number between one and fifty where fifty represents very poor precision and one represents ideal accuracy. Table 2-1 lists what I believe to be an accurate breakdown of DOP values.

  DOP     Rating      Description
   1      Ideal       This is the highest possible confidence level to be used for 
                      applications demanding the highest possible precision
                      at all times.
  2-3     Excellent   At this confidence level, positional measurements are considered 
                      accurate enough to meet all but the most sensitive applications.
  4-6     Good        Represents a level that marks the minimum appropriate for making 
                      business decisions. Positional measurements could be used to 
                      make reliable in-route navigation suggestions to the user.
  7-8     Moderate    Positional measurements could be used for calculations, 
                      but the fix quality could still be improved.  A more open view 
                      of the sky is recommended.
  9-20    Fair        Represents a low confidence level. Positional measurements 
                      should be discarded or used only to indicate a very rough 
                      estimate of the current location.
  21-50   Poor        At this level, measurements are inaccurate by half a football 
                      field or more and should be discarded.

Table 2-1: An interpretation of dilution of precision values.

Looking again at figure 2-1, three satellites created a large area of possible solutions. This situation could be improved by two factors: adding more satellites to the fix, and using satellites evenly distributed throughout the sky. What would figure 2-1 look like if the situation was improved like this? Figure 2-2 shows figure 2-1 after three more evenly-distributed satellites have been added.

Figure 2-2: Three more evenly-distributed satellites are added to figure 2-1, creating a high-precision environment where dilution of precision is low.

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