Position Estimation from UWB Pseudorange and Angle-of-Arrival: A Comparison of Non-linear Regression and Kalman Filtering

This paper presents two algorithms, non-linear regression and Kalman filtering, that fuse heterogeneous data (pseudorange and angle-of-arrival) from an ultra-wideband positioning system. The performance of both the algorithms is evaluated using real data from two deployments, for both static and dynamic scenarios. We also consider the effectiveness of the proposed algorithms for systems with reduced infrastructure (lower deployment density), and for lower-complexity sensing platforms which are only capable of providing either pseudorange or angle-of-arrival

Statistical Analysis

In this Appendix, we provide an outline of methods used in analyzing molecular biology data. We give a summary of types of data encountered and the appropriate methods to apply for the questions of interest. Statistical techniques described include the t test, the Wilcoxon rank sum test, the Mann-Whitney-Wilcoxon test, ANOVA, regression, and the chi-square test. For each method, we give the appropriate assumptions, the details of the test, and a complete concrete example to follow. We also discuss related ideas such as multiple comparisons and why correlation does not imply causation