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I've managed to resolve most of the NaNs by taking only the best possible landmark estimates for each target and/or by eliminating the "bad pairs". Bullets below explain the activity:
- There were a total of 67 NaN values for multilateration.
- Now only 14 NaN values remain for multilateration: 11 are common with trilateration and 3 are unique.
- 39 NaNs removed by keeping number of estimates, n = 10.
- 14 NaNs removed by keeping number of estimates, n = 4.
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A sample of "bad pairs" from target "132.248.120.214" is below. Each line contains four values separated by white-space. First line contains: target-lat target-long id rtt. All other lines contain: landmark-lat landmark-long rtt id.
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- There are a total of 174 targets for CBG out of which 131 remain after ignoring values that either have error in the range "error<1 kmerror<1km" (i.e. the target and at least one landmark are probably in the same location) or contain "NaN".
- Improved trilateration by Farrah produced results for only 78 targets so far. Her method produced 7 NaN values which she ignored.
- Only 74 targets overlap between CBG trilateration and improved trilateration.
- If I don't ignore CBG's values that have estimate error in the range "0<error<1error<1km" then CBG trilateration performs 64/74 times better and improved trilateration performs only 10/74 times better.
- Even if I ignore values with error estimate "error<1km" then CBG performs 32/74 times better, improved trilateration performs 10/74 times better and the rest are unaccounted for.