Multidimensional Scaling And Biased Locating Method Research

With the rise of the concept about Internet of Things, wireless location techniques have become more and more important in people’s daily lives and the field of the national defense. While bringing challenges to conventional location technology, the diversity of demand and the complexity of the location environments promote the continuous development of the positioning techniques.This thesis gives the basic positioning models and algorithms firstly, and based on which the fast MDS (Multidimensional Scaling) location algorithm and the biased location algorithm are proposed. The main works are included as following:1. We introduce the background, research status and development trends of the wireless location techniques briefly, and categorize the location algorithms according to the characteristic parameters and statistics.2. The positioning models and algorithms based on time-of-arrival (TOA), time-difference-of-arrival (TDOA) and received-signal-strength (RSS) are described. The squared range measurements based least squares (SR-LS) and the squared range difference measurements based least squares (SRD-LS) can produce the exact solutions of the TOA-based and TDOA-based positioning models respectively. Based on the principle of two-step weight least squares (TWLS), a new improved RSS-based localization algorithm is developed and its performance achieves the Cramer-Rao lower bound (CRLB) at sufficiently small noise conditions.3. The multidimensional scaling (MDS) algorithm has been discussed detailly. Since the existing MDS localization algorithm cannot meet the continuously track owing to large amount of computation, we propose a new fast algorithm based on Lagrange multiplier method and matrix decomposition. The analysis shows that our algorithm decreases the compute capacity greatly due to avoiding eigenvalue decomposition and iterative operation.4. To locate the localization node (LN) distributed in a given prior area more accurately we propose two new localization algorithms based on biased estimation. The simulation result shows that the two algorithms are superior to the traditional least squares algorithm and the traditional CRLB can’t be used as a benchmark here because of allowing the bias errors.