Algorithm Research For Signal Processing In Through Wall Life-detection Radar

Through-the-wall Detection Radar (TWDR) is widely used in rescuing human victims buried after earthquake. The radar emits a microwave beam to penetrate the wall and reach the subject, the reflected wave from the human body is received and heartbeat and respiration signals are extracted. One challenge is how to get the breathing and heartbeat information from the echo signal because vital signals are weak, time-varying and non-stationary signals.In this thesis, Continuous Wave TWDR and Impulse-based UWB TWDR have been applied to detect vital signals. Advanced signal processing methods, such as high-resolution spectral estimation, adaptive signal processing, blind separation algorithm, pattern recognition, are used for vital signals detection. These have the potential to not only improve the robustness of Doppler radar sensing to practical levels, but to also make possible the gathering of additional information, such as determining the number of subjects in a particular environment and recognise people and animals.The work of this thesis is reflected in the following areas:Firstly, a new method to suppress the direct wave in impulse-based UWB radar system is proposed. Wide-band cross correlation methods is used to estimate the echo delay. Then, select mean and variance of the delay sequence as features and use C-Means clustering algorithm to achieve echo classification, so direct wave is removed effectively. The results indicate that the proposed method more effective than mean-subtraction method to suppress the direct wave. Secondly, in order to overcome the shortcomings that the conventional vital signal extraction algorithms based on FFT are not good at dealing with non-stationary signals and susceptible to harmonic interference, a new method for vital signal extraction based on empirical mode decomposition is proposed in this paper. The proposed algorithm greatly improves the estimation accuracy of respiratory rate and heart-rate. Lastly, we establish radar hardware test platform based on Labview. Combining simulation and measured data to verification and improve the algorithms, which provide theoretical support for achieving practical radar detector.