On the Statistical Errors of RADAR Location Sensor Networks with Built-In Wi-Fi Gaussian Linear Fingerprints

The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis

Novel Methods for Personal Indoor Positioning

Currently, people are used to getting accurate GNSS based positioning services. However, in indoor environments, the GNSS cannot provide the accuracy and availability comparable to open outdoor environments. Therefore, alternatives to GNSS are needed for indoor positioning. In this thesis, methods for pedestrian indoor positioning are proposed. With these novel methods, the mobile unit performs all the required positioning measurements and no dedicated positioning infrastructure is required.This thesis proposes novel radio map configuration methods for WLAN fingerprinting based on received signal strength measurements. These methods with different model parameters were studied in field tests to identify the best models with reasonable positioning accuracy and moderate memory requirements. A histogram based WLAN fingerprinting model is proposed to aid IMU based pedestrian dead reckoning that is obtained using a gyro and a 3-axis accelerometer, both based on MEMS technology. The sensor data is used to detect the steps taken by a person on foot and to estimate the step length and the heading change during each step.For the aiding of the PDR with WLAN positioning, this thesis proposes two different configurations of complementary extended Kalman filters. The field tests show that these configurations produce equivalent position estimates. Two particle filters are proposed to implement the map aided PDR: one filter uses only the PDR and map information, while the other uses also the WLAN positioning. Based on the field tests, map aiding improves the positioning accuracy more than WLAN positioning.Novel map checking algorithms based on the sequential re-selection of obstacle lines are proposed to decrease the computation time required by the indoor map matching. To present the map information, both unstructured and structured obstacle maps are used. The feasibility of the proposed particle filter algorithms to real time navigation were demonstrated in field tests