Study Of UWB Through-wall Imaging And Doppler Characteristic

Because the ultra wide band (UWB) through wall radar has the ability of moving targets detection, localization, imaging and tracking behind the buildings or the barriers, it is widely used in military devices, city security, person secure at fire or earthquakes, etc. In order to obtain good performance, all kinds of key parameters should be considered seriously in the UWB through wall radar system design. Besides that, signal processing is also an important aspect. In this thesis, four important contents including targets localization, imaging, Doppler characteristic analysis and targets movement classification are studied. The main innovations of this thesis are as follows:1. For wall existing situation, two localization methods are proposed, which are the method of getting the numerical value of nonlinear localization equations using Newton method and the method of searching the position of the target in two dimentional geometry space. Simulation results of the target localization are given using the radar with one transmit antenna and two received antennas. Noise effect to the target localization precision is analyzed. The issues of wall parameters estimation and the target localization with unknown wall parameters (the wall thickness and dielectric constant) are discussed and simulation results are given.2. For a room with complex background such as full of furniture with desks and chairs, using the back projection (BP) algorithm the moving target of the human can not be distinguished from other background objects. To solve the problem, the moving target BP algorithm is proposed. For the UWB pulse through wall radar, using the BP algorithm to the difference signal obtained by subtracting the previous received signal from the current received signal, the image of background is eliminated greatly and the image of the moving target can be seen clearly. For the UWB noise through wall radar, by subtracting the previous frame cross-correlation functions between the received signal with the transmit noise signal from current frame cross-correlation functions, the coupling signal between the transmit and receive antennas as well as the background clutter including the wall and the furniture behind the wall are eliminated greatly and the reflection information of the moving target is reserved. Using the BP algorithm to the difference signal the cross-correlation functions of two successive frame samples, the image of the moving target is obtained and the signal to clutter ratio of the image is improved greatly. For above two radar systems, by refreshing difference images on the screen sequentially and adding all the images of the moving target together, we can get the track of the moving target. Many through wall senarioes are simulated using the finite difference time domain (FDTD) method for two radar systems. The simulation results show that the moving target BP algorithm is valid for human detection, localization, imaging and tracking in complex environment.3. Because the mode mixing problem may exist in the empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) method doesn't have good effect on noise suppression in the signal, an improved EEMD method is proposed. This method is applied to the Doppler characteristic analysis of the human movements and the time-frequency-energy spectrum is obtained by Hilbert-Huang transform (HHT). Simulation and experimental results show that the improved EEMD method can not only eliminate the mode mixing problem by decomposing different Doppler frequency components to different intrinsic mode functions (IMFs), but also suppress the noise in the signal effectively so that more detail information can be seen clearly in the time-frequency spectrum.4. The classification of different kinds of movements of the human is realized by EMD combined with the method of support vector machine (SVM). Five types of movements of the human including standing still, standing with arms waving, one stepping forward and back, walking and running are analized respectively by EMD, EEMD and improved EEMD. The IMFs of each type of movement are obtained by three decomposition methods. Selecting the energy ratio of each IMF to the total IMFs as the feacture vector, using the SVM to the 564 group experimental samples, different types of movements of the human can be recognized and the biggest correct recognition rate is above 94%.