Xiaolin Liang

Improved denoising method for through-wall vital sign detection using UWB impulse radar

Abstract Noncontact vital sign detection is widely used in finding victims in post-disaster search and rescue, through-wall surveillance (TWS), and medical diagnosis and monitoring. Human subject can be remotely sensed by extracting life activities such as respiration and heartbeat. However, the signal to noise ratio (SNR) is often low, particularly in complex environments, which results in errors in both range and respiration frequency (RF) estimation. To improve the accuracy, an improved system for the vital sign detection is presented which is based on impulse ultra-wideband (UWB) radar. The range is determined based on the short-time Fourier transform (STFT) of the standard deviation of the received signals. Further, the ensemble empirical mode decomposition (EEMD) based frequency accumulation (FA) technique is presented to determine RF. Performance results are presented which show that the proposed method is superior to several well-known techniques.

Extreme learning machine for 60 GHz millimetre wave positioning

Extreme learning machine (ELM) has attracted considerable attention in recent years due to its numerous applications in classification and regression. In this study, the authors investigate the performance of an ELM-based threshold selection algorithm for 60 GHz millimetre wave time of arrival estimation using energy detector (ED). A hybrid metric based on the skewness, kurtosis, standard deviation, and slope of the ED values is employed. The optimal normalised threshold for different signal-to-noise ratios (SNRs) is investigated, and the effects of the integration period and channel model are examined. Performance results are presented which show that the proposed ELM-based algorithm provides high precision and better robustness than existing techniques over a wide range of SNRs for the IEEE 802.15.3c CM1.1 and CM2.1 channel models. Further, the performance is largely independent of the integration period and channel model.

An Improved Algorithm for Through-Wall Target Detection Using Ultra-Wideband Impulse Radar

This paper considers the detection and localization of a human subject in complex environments using an ultra-wideband impulse radar. The subject is remotely sensed by extracting micro-motion information, such as the respiration and heartbeat frequencies. It is challenging to extract this information due to the low signal to noise and clutter ratio in typical disaster environments. To improve the localization accuracy, a new method is proposed using the characteristics of vital sign signals. The range is determined using a short-time Fourier transform of the kurtosis and standard deviation of the received signals. Furthermore, an improved arctangent demodulation technique is used to determine the frequency of human micro-motion based on a multiple frequency accumulation method. Performance results are presented, which show that the proposed method is superior to several well-known techniques.

Ultra-wide band impulse radar for life detection using wavelet packet decomposition

Abstract This paper develops an improved algorithm for life detection based on ultra-wide band (UWB) impulse radars in through-wall condition. Usually, the cardiac–respiratory movement is the most significant sign of a living person, which can be extracted from the collected pulses. The proposed algorithm analyzes the variances of the collected pulses, which are mixed with various clutters. The time of arrival (TOA) between UWB radar and human target can be calculated by performing the wavelet packet decomposition on the calculated variances. The rates of human cardiac–respiratory movement are acquired based on the variable time-window (VTW) method. Compared with several well-known algorithms, the developed method can provide competitive performance in life detection. Results acquired from different sites prove the efficiency and reliability of the method.

An improved energy detection receiver for toa estimate in mm-Wave system

It is challenging to acquire the first arriving path for the estimate of the time of arrival (TOA) in multipath channels, particularly for low signal to noise ratio. The energy detection (ED) receiver is a better scheme for TOA estimate in millimeter-wave (mm-Wave) ranging system since its simple circuit structure. However, the traditional algorithms for TOA estimate using ED cannot provide the adequate accuracy. In this paper, a novel ED receiver for mm-Wave TOA estimate is developed by employing the higher order cumulant technique. Results acquired in IEEE 802.15.3c models confirm the effectiveness and better performance of this solution.

Experimental Study of Wireless Monitoring of Human Respiratory Movements Using UWB Impulse Radar Systems

This paper analyzes and discusses the capability of human being detection using impulse ultra-wideband (UWB) radar with an improved detection algorithm. The multiple automatic gain control (AGC) technique is employed to enhance the amplitudes of human respiratory signals. Two filters with seven values averaged are used to further improve the signal-to-noise ratio (SNR) of the human respiratory signals. The maximum slope and standard deviation are used for analyzing the characteristics of the received pulses, which can provide two distance estimates for human being detection. Most importantly, based on the two distance estimates, we can accurately judge whether there are human beings in the detection environments or not. The data size can be reduced based on the defined interested region, which can improve the operation efficiency of the radar system for human being detection. The developed algorithm provides excellent performance regarding human being detection, which is validated through comparison with several well-known algorithms.

Ultra-Wideband Impulse Radar Through-Wall Detection of Vital Signs

This paper presents a new system for the detection of human respiration behind obstacles using impulse ultra-wideband (UWB) radar. In complex environments, low signal-to-noise ratios (SNRs) as they can result in significant errors in the respiration, heartbeat frequency, and range estimates. To improve the performance, the complex signal demodulation (CSD) technique is extended by employing the signal logarithm and derivative. A frequency accumulation (FA) method is proposed to suppress mixed products of the heartbeat and respiration signals and spurious respiration signal harmonics. The respiration frequency is estimated using the phase variations in the received signal, and a discrete short-time Fourier transform (DSFT) is used to estimate the range. The performance of the proposed system is evaluated along with that of several well-known techniques in the literature.

A 60 GHz impulse radio positioning algorithm based on a BP neural network

Recent developments in wireless communications have led to numerous applications that require accurate positioning. Further, people are becoming more dependent on location based services. Many of these services are located indoors. Because of the complexity of indoor channels, traditional algorithms cannot accurately estimate the signal attenuation, which degrades positioning accuracy. To overcome this problem, a 60 GHz Impulse Radio (IR) algorithm based on a Back Propagation Neural Network (BPNN) is proposed in this paper. Simulation results are presented which show the the positioning accuracy of the proposed algorithm is better than with other localization algorithms.

A New Low Complexity NLOS Identification Approach Based on Maximum Slope and Skewness of Energy Block for 60GHz System

The major problem of indoor localization is the presence of non-line-of-sight (NLOS) channels. In order to perform the NLOS identification, in this paper, we propose a novel NLOS identification technique based on the ratio values of the maximum slope and skewness of energy block of the received signal using energy detector. In particular, the IEEE 802.15.3c 60 GHz channel models are used as examples and the above statistics is found to be explained in detail. The simplicity of the proposed approach lies in the use of the parameters of the energy-based time of arrival (TOA) Estimation algorithm. The CM1 (LOS) and CM2 (NLOS) channel models of the standard IEEE 802.15.3c channel models are used. Numerical simulations results show that the correct identification of channel models with the proposed approach is better than with the multipath channel statistics based approach.

Research on NLOS Channel Based on IEEE 802.15.3c Standard

The major problem of indoor localization is the presence of non-line-of-sight (NLOS) channels. In order to perform NLOS identification, a novel NLOS identification technique is proposed which is based on the multiply of the standard deviation, max-slope and max-gradient of the block using energy detector (ED). IEEE 802.15.3c 60 GHz channel models are used as examples and the above statistics are explained in detail. The simplicity of proposed approach lies in ED-based algorithm. The LOS and NLOS of IEEE 802.15.3c channel models are used. Simulations results show the correct identification is better than with the multipath channel statistics based approach. Introduction The 60 GHz has a great potential for accurate ranging and localization systems due to its very wide bandwidth and capability in resolving individual multipath components [1–3]. Therefore, TOA can be estimated with high accuracy for the 60 GHz systems if the first arriving path has been identified precisely. One of the major challenges for localization systems is mitigation of NLOS effects. If the direct path between a fixed terminal and mobile terminal is being obstructed, TOA to the fixed terminal will be delayed, which introduces a positive bias. Using such NLOS TOA, the mobile terminal position may significantly degrade positioning accuracy. The non-line of sight identification techniques have been discussed extensively in the literature, but mainly within the cellular network framework [4–10]. For example, in [7], the authors address the NLOS identification problems based on the multiple received signal strength measurements from Wi-Fi signals. The key to the approach is to exploit several statistical features of the RSS time series. Shimizu et al. [9] performed intensive measurements of path-loss and delay-profile characteristics of line-of-sight and non-line-of-sight environments in a suburban residential area. Based on their analysis, they found that the delay spread was dependent on distance, and the non-line-of-sight delay spread was found to be several times larger than that of the line-of-sight case. The skewness of delay spread for the non-line-of-sight cases ranged from 80 to 200ns, which was an order of magnitude larger than that of the line-of-sight case. In this paper, a new NLOS identification approach is proposed for the 60 GHz signal, which is based on the standard deviation, max-slope, max-gradient of the energy block using ED. Firstly, we use ED-based algorithm for TOA estimation. Secondly, we characterize the standard deviation, max-slope, max-gradient of the block. Finally, we use a threshold test for NLOS identification. The remainder of this paper is organized as follows. Section 2 describes signal and channel model. Section 3 describes NLOS identification approach and Section 4 presents results of numerical simulations. The concluding remarks are given in section 5. System Model The 60 GHz signals have a very short duration and which can be expressed as: ( ) ( ) s j c j s t p t jT C T a ε ∞ −∞ = − − − ∑ (1) 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)