Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Design of indoor positioning systems based on location fingerprinting technique
Design of indoor positioning systems based on location fingerprinting technique
Ultra-wideband geo-regioning: a novel clustering and localization technique
EURASIP Journal on Advances in Signal Processing
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks: The International Journal of Computer and Telecommunications Networking
Geolocation in mines with an impulse response fingerprinting technique and neural networks
IEEE Transactions on Wireless Communications
Analysis of wireless geolocation in a non-line-of-sight environment
IEEE Transactions on Wireless Communications
Ultrawideband Channel Modeling on the Basis of Information-Theoretic Criteria
IEEE Transactions on Wireless Communications
Energy-Detection UWB Receivers with Multiple Energy Measurements
IEEE Transactions on Wireless Communications
Ranging in a dense multipath environment using an UWB radio link
IEEE Journal on Selected Areas in Communications
Computationally tractable model of energy detection performance over slow fading channels
IEEE Communications Letters
A Maximum Likelihood UWB Localization Algorithm Exploiting Knowledge of the Service Area Layout
Wireless Personal Communications: An International Journal
Theoretical entropy assessment of fingerprint-based Wi-Fi localization accuracy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 35.69 |
In this paper, we propose and investigate location fingerprinting with a low complexity generalized ultrawideband (UWB) energy detection receiver. The energy samples at the output of the analog receiver front-end serve as location fingerprints. We formulate the position location problem as hypothesis testing problem and develop a Bayesian framework treating the location fingerprints as random vectors. In order to obtain an accurate stochastic description of the energy samples, which is required by the Bayesian framework, we provide two approaches. First, we derive a numerical algorithm to calculate the exact probability density functions of the energy samples, in case the UWB channel follows a Gaussian process. These results are used for benchmarking and performance prediction. Second, we propose closed form probability density functions based on a model selection criterion and measured energy samples. We show the accuracy and applicability of these closed form probability density functions in terms of performance results of the position location algorithm. The performance of the proposed location fingerprinting algorithm is evaluated based on measured UWB channels. The impact of important system parameters on the performance is investigated as well.