Parameter Localization Based On Multidimensional Scaling Research

With the rise of the concept about Internet of Things, location-aware services incivil and military fields will play an important role, while the traditionallocation-aware services rely on positioning satellite or cellular networks, cannot meetthe needs of positioning in a complex environment or high-precision positioning.This dissertation is to address this problem. Based on an in-depth analysis ofchannel transmission characteristics in the indoor environment, and a hardwareplatform on ZigBee technology for wireless self-organizing network (Wireless AD HocNetworks), a detailed study of multidimensional scaling (MDS) localization algorithmhas been conducted. The dissertation in the field of indoor environment localizationwas innovative and exploratory research. The main contents are:1. Detailed study of existing MDS location algorithms for the unified solutionframework and its general solution. The traditional MDS localization framework usingeigenvalue decomposition or inverse operation, cannot meet the need of continuouslymobile station track. To address this issue, a fast localization algorithm is introduced.The proposed method which based on the MDS matrix decomposition and Lagrangeconstraint functions, is effective to avoid the eigenvalue decomposition existed inMDS methods, and simplifies the computational complexity.2. Detailed study of the characteristics of unbiased estimate, as well as theCramer-Rao Lower Bound (CRLB). Draws on the two-step weighted least squares(TWLS) method, a weighted MDS fast localization algorithm based on time of arrival(TOA) measurements is proposed. The results showed that the localizationperformance of the weighted MDS fast localization algorithm is close to the CRLB inthe medium SNR level, and unified under the framework of weighted MDS.3. For the low robust problem of classic MDS localization algorithm in low SNRconditions, the noise vector least square solution based on MDS localization algorithmis proposed. The algorithm showed a more robust performance in low signal to noiseratio (SNR) and fewer base stations, compared with the classical MDS localization algorithm and subspace MDS localization algorithm.4. MDS localization method is innovatively introduced into the arrival of angle(AOA) measurements. Compared with the traditional least squares location algorithm,the proposed estimator is superior in the condition of changed SNR and base station(BS).5. Complex representation is firstly introduced into MDS algorithm. Theimaginary part informative rich distance features about location of mobile station (MS).Through simulation analysis, we can find that the proposed method which is close toCRLB, covers more distance information in the complex data matrix, and itslocalization performance is superior to the traditional MDS localization algorithm.6. Detailed analysis of the lognormal model conditional estimators and wirelessZigBee technology-based AD Hoc Networks platform is introduced. Rely onMATLAB GUI, we build an indoor localization software system with the measurementof received signal strength (RSS). Maximum likelihood estimation combined with thesteepest gradient descent positioning algorithm is the key to position estimation. In theindoor environment, the positioning accuracy is better than existed indoor positioninghardware engine.