Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method

Indoor localization promises a lot of benefits on the application in various fields. The fingerprint method is often used because it has high mobility, low network cost, and high compatibility. However, the distance and RSSI relationships are non-linear which decreases the accuracy of the system. KNN is required as a matching algorithm to solve the problem. The error result of Fingerprint-KNN system for indoor localization is still less satisfactory, therefore weighting factor is added in KNN algorithm as a modification to optimize the accuracy and precision of the localization system. The usual W-KNN is adding a value in form of the distance error from estimation result. In this paper, the constant as the result of iteration process within a range is multiplied by the error value which is added to the system as a weighting of KNN algorithm. The iterated weighting constant provides optimization on the system up to 25% better than the conventional system

Towards fast hybrid deep kernel learning methods

El treball estudia la millor manera de crear xarxes neuronals híbrides amb mètodes kernel mitjançant dues aproximacions de kernel diferents, random Fourier features i el mètode Nystrom, i la millor manera d'entrenar-les, amb RMSprop i stochastic gradient descent

A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization

In accordance with the problem that the traditional trilateral or multilateral estimation localization method is highly dependent on the proportion of beacon nodes and the measurement accuracy, an algorithm based on kernel sparse preserve projection (KSPP) is proposed in this dissertation. The Gaussian kernel function is used to evaluate the similarity between nodes, and the location of the unknown nodes will be commonly decided by all the nodes within communication radius through selection of sparse preserve projection self-adaptation and maintaining of the topological structure between adjacent nodes. Therefore, the algorithm can effectively solve the nonlinear problem while ranging, and it becomes less affected by the measuring error and beacon nodes quantity