The location estimate using the M-estimator is obtained in the following: begin{array}rcl@ widehat{{x}}^{mathrm{M}}=min_{{x}}sum_{{i}=1}^{{M}} rholeft{frac{ {b}_{{i}}-{a}_{{i}}^{T}{x}}{s}right} end{array} (6).
where are the estimated distances by using the initial location estimate.
As with LS, the rank-based estimator of the intercept is a location estimate based on the residuals.
See Appendix A. In this section we use the GCD ML location estimate cost function to define an -type estimator.
We utilize the Minimum Covariance Determinant (MCD) estimator [16] which yields a robust location estimate for each window that is not highly influenced by outlying locations caused by the limitations of Argos.
It can be noticed that (28) is the implicit equation for the M-GC estimator with as defined in (18), implying that the location estimate has the same properties derived above.
Repeat multiple times to determine the error in the software location estimate.
In such cases, a fresh snapshot and a comparison of the visual features with those already stored can help correct its location estimate.
The other high performing approach was based on moving in the direction which led to the largest reduction in the uncertainty of the location estimate.
In many situations, however, landscape/map information can help a third party to perform Bayesian inference over spatially obfuscated data and to refine the user's location estimate up to a violation of the original user's location-privacy requirements.
If the person wearing the sensors returns to an area that he or she has previously visited, the system's location estimate could be off: For instance, its compensation for the tilt of the rangefinder might not have been perfect, and a wall now looks several feet farther away than it did, or its inference of position from accelerometer data could be off.
