A new cooperative received signal strength-based localization algorithm is proposed which employs relative error estimation and semidefinite programming (SDP). First, the log-normal shadowing RSS measurement model is transformed into an equivalent multiplicative model. Then, a relative error estimation criterion is used with this model to develop a nonconvex estimator to approximate the maximum likelihood solution. Finally, semidefinite relaxation is applied to the nonconvex estimator to obtain an SDP estimator. The proposed algorithm is first derived for noncooperative RSS-based localization and then extended to the cooperative case. The Cramer-Rao lower bound is derived for cooperative RSS-based localization. Performance results are presented, which demonstrate that the proposed SDP estimator provides a significant improvement over existing localization methods.

Ultra-wideband (UWB) impulse radar plays an important role in contactless vital sign (VS) detection. The VS can be extracted remotely by acquiring the oscillations in the human chest. Unfortunately, it is usually challenging to identify VS due to the low signal-to-noise ratio (SNR) only based on the traditional fast Fourier transform (FFT) especially in complicated conditions. To extract VS accurately, this paper presents a new scheme by analyzing the skewness characteristic of the received UWB impulses, which are modulated by life activities. The distance from the human subject to the radar antenna can be calculated by performing the discrete short-time Fourier transform (DSFT) on skewness. The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD)-based accumulation technique by canceling out the harmonics effectively. The performance of the developed detection method is tested with several experiments carried out in different environments.

This paper considers vital signs (VS) such as respiration movement detection of human subjects using an impulse ultra-wideband (UWB) through-wall radar with an improved sensing algorithm for random-noise de-noising and clutter elimination. One filter is used to improve the signal-to-noise ratio (SNR) of these VS signals. Using the wavelet packet decomposition, the standard deviation based spectral kurtosis is employed to analyze the signal characteristics to provide the distance estimate between the radar and human subject. The data size is reduced based on a defined region of interest (ROI), and this improves the system efficiency. The respiration frequency is estimated using a multiple time window selection algorithm. Experimental results are presented which illustrate the efficacy and reliability of this method. The proposed method is shown to provide better VS estimation than existing techniques in the literature.