Qingchao Chen

Indoor target tracking using high doppler resolution passive Wi-Fi radar

This paper describes two Doppler only indoor passive Wi-Fi tracking methods based on high Doppler resolution passive radar. Two filters are investigated in this paper, the extended Kalman filter and the sequential importance resampling (SIR) particle filter. Experimental results for these two tracking filters are presented using results from software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator. The experimental results show that the SIR particle filter performs well using Wi-Fi signals for indoor tracking with a high degree of accuracy. Proposals for simplifying the SIR particle and application to multiple target tracking are also discussed.

Wi-Fi based passive human motion sensing for in-home healthcare applications

This paper introduces a Wi-Fi signal based passive wireless sensing system that has the capability to detect diverse indoor human movements, from whole body motions to limb movements and breathing movements of the chest. The real time signal processing is used for human body motion sensing, and software defined radio demo system are described and verified in practical experiments scenarios, which include detection of through-wall human body movement, hand gesture or tremor, and even respiration. The experiment results offer potential for promising healthcare applications using Wi-Fi passive sensing in the home to monitor daily activities, to gather health data and detect emergency situations.

Signs of life detection using wireless passive radar

Non-contact devices for monitoring signs of life have attracted a lot of attention in recent years for applications in security, emergency and disaster situations. Current devices however, generally utilize bespoke active systems to transmit large bandwidth signals. In this paper, a real-time phase extraction method based on passive Wi-Fi radar is proposed for detecting the chest movements associated with a person breathing. Since the monitored movements are of low amplitude and small Doppler shift, this method uses the phase variation rather than traditional range-Doppler processing. The processing is based on time domain cross correlation, with the addition of a Hampel filter for outlier detection and removal. In this paper the basic passive Wi-Fi model and limitations of traditional cross ambiguity function for signs of life detection are first introduced. The phase extraction method is then described followed by experimental results and analysis. Detection of breathing for a stationary person is shown in both in-room and through wall scenarios using both the Wi-Fi beacon and data transmissions. This is believed to be the first demonstration of signs of life detection using phase extraction in passive radar and extends the capability of such systems into a wide range of new applications.

Activity recognition based on micro-Doppler signature with in-home Wi-Fi

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method.

Support Discrimination Dictionary Learning for Image Classification

Dictionary learning has been successfully applied in image classification. However, many dictionary learning methods that encode only a single image at a time while training, ignore correlation and other useful information contained within the entire training set. In this paper, we propose a new principle that uses the support of the coefficients to measure the similarity between the pairs of coefficients, instead of using Euclidian distance directly. More specifically, we proposed a support discrimination dictionary learning method, which finds a dictionary under which the coefficients of images from the same class have a common sparse structure while the size of the overlapped signal support of different classes is minimised. In addition, adopting a shared dictionary in a multi-task learning setting, this method can find the number and position of associated dictionary atoms for each class automatically by using structured sparsity on a group of images. The proposed model is extensively evaluated using various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods.

Dictionary Learning Inspired Deep Network for Scene Recognition

Scene recognition remains one of the most challenging problems in image understanding. With the help of fully connected layers (FCL) and rectified linear units (ReLu), deep networks can extract the moderately sparse and discriminative feature representation required for scene recognition. However, few methods consider exploiting a sparsity model for learning the feature representation in order to provide enhanced discriminative capability. In this paper, we replace the conventional FCL and ReLu with a new dictionary learning layer, that is composed of a finite number of recurrent units to simultaneously enhance the sparse representation and discriminative abilities of features via the determination of optimal dictionaries. In addition, with the help of the structure of the dictionary, we propose a new label discriminative regressor to boost the discrimination ability. We also propose new constraints to prevent overfitting by incorporating the advantage of the Mahalanobis and Euclidean distances to balance the recognition accuracy and generalization performance. Our proposed approach is evaluated using various scene datasets and shows superior performance to many stateof-the-art approaches.

Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Detection and interpretation of human activities have emerged as a challenging healthcare problem in areas such as assisted living and remote monitoring. Besides traditional approaches that rely on wearable devices and camera systems, WiFi-based technologies are evolving as a promising solution for indoor monitoring and activity recognition. This is, in part, due to the pervasive nature of WiFi in residential settings such as homes and care facilities, and the unobtrusive nature of WiFi-based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf devices or bespoke hardware. This includes phase variations, frequency shifts, and signal levels. In this article, we describe the healthcare application of Doppler shifts in the WiFi CSI caused by human activities that take place in the signal coverage area. The technique is shown to recognize different types of human activities and behavior and be very suitable for applications in healthcare. Three experimental case studies are presented to illustrate the capabilities of WiFi CSI Doppler sensing in assisted living and residential care environments. We also discuss the potential opportunities and practical challenges for realworld scenarios.

Joint fall and aspect angle recognition using fine-grained micro-Doppler classification

Activity recognition and monitoring are finding important applications in ambient assisted living healthcare. Among the various types of motions which researchers are attempting to detect and recognize, fall detection has gained significant interest. In this paper, we investigate the application of high-frequency (24 GHz) FMCW radar for multi-perspective micro-Doppler (μ-D) activity recognition. Data from two different types of motion; falling and picking-up an object, were collected from three aspect angles and put through a fine-grained classifier to not only differentiate the motions, but to also identify their aspect towards the radar receivers. A key novel component of this work is the application of the fine-grained classification task, where a label discriminate sparse representation classifier is proposed to improve recognition performance over very similar μ-D signatures. This is achieved by learning a discriminate dictionary constrained by the label information and meanwhile preventing the overfitting problem. The greatest increase in classification performance was found to be of the order of 8 %.

A coherent through-wall MIMO phased array imaging radar based on time-duplexed switching

Through-the-Wall (TW) radar sensors are gaining increasing interest for security, surveillance and search and rescue applications. Additionally, the integration of Multiple-Input, Multiple-Output (MIMO) techniques with phased array radar is allowing higher performance at lower cost. In this paper we present a 4-by-4 TW MIMO phased array imaging radar operating at 2.4 GHz with 200 MHz bandwidth. To achieve high imaging resolution in a cost-effective manner, the 4 Tx and 4 Rx elements are used to synthesize a uniform linear array (ULA) of 16 virtual elements. Furthermore, the transmitter is based on a single-channel 4-element time-multiplexed switched array. In transmission, the radar utilizes frequency modulated continuous wave (FMCW) waveforms that undergo de-ramping on receive to allow digitization at relatively low sampling rates, which then simplifies the imaging process. This architecture has been designed for the short-range TW scenarios envisaged, and permits sufficient time to switch between antenna elements. The paper first outlines the system characteristics before describing the key signal processing and imaging algorithms which are based on traditional Fast Fourier Transform (FFT) processing. These techniques are implemented in LabVIEW software. Finally, we report results from an experimental campaign that investigated the imaging capabilities of the system and demonstrated the detection of personnel targets. Moreover, we show that multiple targets within a room with greater than approximately 1 meter separation can be distinguished from one another.