Study On Bias-suppression Of The Source Localization Under The NLOS Conditions

Source localization problems usually refer to using a set of sensors distributed in the observation space to establish some relative geometric relationships between a source and measurements(such as Time of Arrival(TOA),time Difference of Arrival(TDOA)and Direction of Arrival(DOA))and then achieve the source location estimate.A source localization problem often assumes that the source signal arrives at a sensor in the so-called line-of-sight(LOS)propagation way.This assumption often collapses in practice.For example,in cellular or wireless local area networks(WLAN)positioning problems,usually there is no direct path propagation(or say line-of-sight propagation)between an emitter and a receiver(e.g.,urban and suburban mobile stations)since many obstacles appear in the propagation channels.The source signals have to arrive at a receiver from the reflection or diffraction paths,which is called non-line of sight(NLOS)propagation.The NLOS propagation is the main reason of enlarging the positioning bias,which should be suppressed to improve the positioning accuracy when we design a positioning method.This study focuses on the bias-reduced localization methods for the NLOS localization problems.The main work includes:1.The NLOS localization models are first introduced using TOA and TDOA measurements respectively.Then some bias-reduced TOA-based localization methods are studied and compared through numerical examples.2.Chan's RT method is first studied for reducing localization bias that is caused by the NLOS phenomenon.Then we improve it by using a distance residual random variable and a simplified RT method to avoid a large number of redundant calculations.3.Two convex relaxation algorithms(i.e.,SDR and SCOR,respectively)based on TOA measurements are first introduced to suppress the NLOS bias for the localization problems in the wireless sensor network,which can make full use of the NLOS error information and can provide good positioning performance.Then an improved robust algorithm is achieved based on the above SDR algorithm.4.Inspired by the idea of the TOA-based convex relaxation algorithm,two robust TDOA-based positioning methods are obtained through the RLS convex relaxation procedure.Simulation results show the superiority of these two methods over the general convex relaxation algorithms.