The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

It is necessary to detect the target reflections in ground penetrating radar (GPR) images, so that surface metal targets can be identified successfully. In order to accurately locate buried metal objects, a novel method called the Multiresolution Monogenic Signal Analysis (MMSA) system is applied in ground penetrating radar (GPR) images. This process includes four steps. First the image is decomposed by the MMSA to extract the amplitude component of the B-scan image. The amplitude component enhances the target reflection and suppresses the direct wave and reflective wave to a large extent. Then we use the region of interest extraction method to locate the genuine target reflections from spurious reflections by calculating the normalized variance of the amplitude component. To find the apexes of the targets, a Hough transform is used in the restricted area. Finally, we estimate the horizontal and vertical position of the target. In terms of buried object detection, the proposed system exhibits promising performance, as shown in the experimental results.

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In actual applications of inverse synthetic aperture radar (ISAR), continuous measurements may be impossible or the collection of data during some periods are not valid in a long coherent processing interval (CPI). Hence, it is significant to study the ISAR imaging strategy with a short CPI. Compressive sensing is a recently proposed technique that allows recovering an unknown sparse signal with overwhelming probability from very limited samples. However, the standard compressive sensing framework has been developed for real-valued signals. One disadvantage of this method is that any prior phase information is not exploited, which may improve the reconstruction quality by applying some extra constraints. In this paper, a new strategy for ISAR imaging based on improved compressive sensing is proposed, which transforms the ISAR imaging problem into a joint optimization problem over the representation of the magnitude and phase of the complex-valued scatter coefficient. Because of using phase information in the algorithm, the image quality is improved. Experimental results confirm the effectiveness of the proposed method.

Image modulation represents image by meaningful characters such as image instantaneous amplitude and instantaneous frequency. A perfect reconstruction image modulation method is proposed. In detail, the bidimensional empirical mode decomposition (BEMD) is first improved to adaptively decompose image into monocomponents. Then by the quaternionic analytic method, suitable analytic signals is acquired. A new polar form is further proposed to modulate images, then seven characters are derived including instantaneous amplitude and instantaneous frequencies. We demonstrate the techniques on both synthetic and natural images, depict the needle program of the estimated frequencies and obtain the reconstructions that are the same with the original images. The applications in image segmentation and separation establish the validity of characterizing images of this type as sums of locally narrow band modulated components.