RSS-Based Localization via Bayesian Ranging and Iterative Least Squares Positioning

A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs

“…Moreover, extending a RSS-based technique for 3D localization can introduce higher complexity in computational cost and location accuracy [32]. A. Coluccia and F. Ricciato have proposed in [37] a Bayesian formulation of the ranging problem alternative to the common approach of inverting the Path-Loss formula (while considering Received Signal Strength measurements). Numerical results show that the combination of the proposed approaches improves considerably the accuracy of range-based localization that use RSS with only a slight increase of computational complexity.…”

A RSSI-based parameter tracking strategy for constrained position localization

“…Although LLS estimator can calculate the position in a computational efficiently way, the error is large which is caused by transforming the initial non-linear relationship of the distance between the target and anchor nodes into linear relationship. To overcome this drawback, non-linear least squares (NLS) [22,23] are proposed. NLS techniques give a higher accuracy than LLS but has a higher computation overhead.…”

Opportunistic Time-of-Arrival localization in fully asynchronous wireless networks

“…15.4. Generally speaking, formalized RSSI-based methods rely on the assumption that the average received power is tied to the transmitter-receiver distance through the so called ''Path-Loss Model'' [2][3][4]. Such methods exhibit low accuracy in practice, since in real environments the received power is heavily affected by other channel factors like multipath and antenna patterns, in addition to transmitter-receiver distance [5,6].…”