Key Technology Research Of Human Target Location Based On Radio Tomographic Imaging

Radio tomography Imaging (RTI) is a new wireless measurement localization technology,which is based on the theory of the radio signal propagation. It has lots of advantages, suchas non-intrusive, non-radiative, insensitive to fog and haze, low cost, simple structure,extensive adaptability and visualization measurement. It has development prospect insecurity monitoring, locating and tracking and medical monitoring.For convenience of mathematical treatment, the conventional RTI technology isoff-line processing for collected data where the targets are moving in imaging area underthe assumption of known background information. The conventional RTI technology doesnot solve the problem of locating stationary human targets where blind backgroundinformations and static human target in imaging area. This research focuses on keytechnology for RTI in this case, including self-positioning transceiver reference node,numerical simulation to the background information in the measured area, fast imaging forstationary human targets in imaging area and noise supression processing of thereconstructed image. The main innovative works of this paper can be summarized asfollows:(1)Superior bidirectional correction localization algorithm based on received signal strengthindicator is presented for the need of prior calibration positioning of transceiver referencenode in RTI system. The algorithm assumes that acquisition nodes of the four corners of therectangular area are anchor nodes and the other unknown nodes are located on the boundary,optimizes the nearest two nodes for node to be positioned and locates its side of therectangular area, gets the initial coordinates of the preferred two anchors and corrects theinitial coordinate by reusing the location information of other two nodes in forward link andreverse link. Comparing with a variety of algorithms, positioning effect of measured datashows that this algorithm can quickly locate, and its positioning accuracy is better thanseveral other algorithms.(2) On the base of the statistical analysis to the RSSI data in the testing system of RTI, wedesign three simulation models under the influence of static obstacles and fading channel inthe small scale. Each transceiver wireless link between nodes can map to one of Rice,Rayleigh and lognormal fading channel statistical models when there are static obstacles inmeasured area. Based on these three simulation models, numerical simulation results of nobody, single person and two persons in measured area show that the degree of matchingbetween simulated values and measured data is high and these models can be wellestimated the background information of static obstacles.(3)Perturbation variance which is created by breathing exercises of stationary human targetsin wireless links is to identify the basis for disturbing link, we can put forward perturbationlink cluster imaging algorithm. The algorithm is based on the calculation of sliding variancein each link, the perturbation variance threshold in different scenarios is determined by itsupper and lower limits, and then identify a few of links which are influenced by stationaryhuman targets, using the link cluster can reduce the computational complexity ofreconstruction algorithm, at the same time, it can achieve imaging rapidly. The algorithmabandons the background information for RTI imaging algorithm to achieve the staticpositioning for the stationary human targets through the body’s own physiological activities.(4)In order to highlight the target body in the reconstructed images, we propose theadvanced adaptive threshold algorithm, which is based on redundant information. Differentfrom computing grayscale threshold from pictorial information traditionally, the algorithmadaptively chooses the gray threshold from the measured data of redundant information,which is used to suppress the noise in the process of reconstructed image. Imaging resultsshow that this algorithm not only can choose the good gray threshold before imaging, butalso can restrain the noise reasonably better than the empirical value method and the OTSUadaptive threshold algorithm. Meanwhile, it can highlight the target body.