Ultra-wideband Radar Signals Research Articles

A non-invasive technique for monitoring supply voltage variation to single phase induction motor using doppler UWB radar signal analysis

The induction motors (IMs) are important components of various industries. The condition of IMs monitored continuously using contact and non-contact methods continuously. The contact methods are expensive, complex and difficult to implement. This paper proposes a non-contact method of fault identification in the single-phase induction motor operating in different conditions by analyzing doppler ultra-wide band (UWB) radar signal. The UWB radar generates a high frequency signal, which is transmitted on to the Induction motor form transmitter and software phase locked loop (SPLL) condition received signal from the receiver. The PLL implements as low pass filter, receives reflected signal from an induction motor along with high frequency noise. The received signal filtered to remove the high frequency noise and filtered output is analyzed to identify the different faults such as over voltage faults and under voltage faults when the motor is running with high, medium and low speed. The proposed non-invasive method has advantages compared to other such as the sensor’s sensitivity will not affect with motor temperature and accuracy will not change with position of the sensor and presence of other machines.

Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD

As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.

Research on real-time elimination of ultra-wideband radar ranging abnormal value data

Abstract. For indoor positioning, ultra-wideband (UWB) radar comes to the forefront due to its strong penetration, anti-jamming, and high-precision ranging abilities. However, due to the complex indoor environment and disorder of obstacles, the problems of diffraction, penetration, and ranging instability caused by UWB radar signals also emerge, which make it difficult to predict the noise and leads to a great impact on the accuracy and stability of the measurement data in the short term. Therefore, the abnormal value migration of the positioning trajectory occurred in real-time positioning. To eliminate this phenomenon and provide more accurate results, the abnormal values need to be removed. It is not difficult to eliminate abnormal values accurately based on a large number of data, but it is still a difficult problem to ensure the stability of the positioning system by using a small number of measurement data in a short time to eliminate abnormal value in real-time ranging data. Thus, this paper focuses on the experimental analysis of a UWB-based indoor positioning system. By repeatedly measuring the range , a large number of measurement data can be obtained. Using the massive data to train linear regression models, we get the parameter of the linear model of range data measured with the UWB radar. Based on the Gaussian function outlier detection, abnormal values are eliminated, and putting the new range data into the regression model trained by us, the ranging error is reduced by nearly 50 % compared with the peak and mean ranging errors in general.

Inhalation and Exhalation Detection for Sleep and Awake Activities Using Non-Contact Ultra-Wideband (UWB) Radar Signal

Respiratory is one of the vital signs used to monitor the progression of the illness that are important for clinical and health care fields. From home rehabilitation to intensive care monitoring, the rate of respiration must be constantly monitored as it offers a proactive approach for early detection of patient deterioration that can be used to trigger therapeutic procedures alarms. The use of invasive procedures based on contact transducers is typically necessary to measure the quantity. Nevertheless, these procedures might be troublesome due to the inconvenience and sensitivity of physical contact. Therefore, non-contact human breathing monitoring as a non-invasive procedure is important in long term intensive-care and home healthcare applications. In this paper, respiratory signals from two type of resting activities had been acquired and proposed a Deep Neural Network (DNN) model that can classify the respiratory signal into inhalation and exhalation signal. Several pre-processing techniques has been done onto the signal before it is implemented into the proposed model. The average recognition rate of the respiratory signal using the proposed method was 84.1% when the subject was sleeping and 83.8% when awake.

Quantitative Evaluation of Channel Micro-Doppler Capacity for MIMO UWB Radar Human Activity Signals Based on Time–Frequency Signatures

A novel quantitative method to evaluate channel micro-Doppler capacity of multiple-input and multiple-output (MIMO) system is proposed here. The method is valid for ultrawideband (UWB) MIMO radar human activity systems based on time–frequency signatures, and the quality measure will be noted as a relative signal to noise ratio (RSNR). The method quantifies these signatures and evaluates the relative superiority or inferiority of these MIMO channels. Examples of micro-Doppler signature ( $\mu $ Ds) characteristics of human activities in a channel will be considered and compared to that of all other channels. First, the MIMO UWB radar human activity signal is modeled, and its corresponding time–frequency ( $T$ – $F$ ) characteristics are analyzed to justify the rationality of using the new RSNR metric. Second, the method is evaluated using experimental data and the capability of distinguishing the $\mu \text{D}$ capacity differences among channels is demonstrated. This new method clearly and accurately shows much better visible $\mu \text{D}$ evaluation performance than that of the conventional signal to noise ratio in time domain ( ${\mathrm {SNR}}_{t}$ ). Moreover, this evaluation method can still work well, even for signals with low signal to noise ratio (SNR) down to −4-dB level. Therefore, it can be successfully used to select the superior channels and eliminate any inferior channels or provide confidence coefficients for the collected multiple channel data of human activities. This method should lead to a significant reduction of the inferior channels’ influence on further MIMO-based classification or imaging of human activities.

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition.

As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

MEASUREMENT OF THE VARIATIONS OF THE PHASE OF A SIGNAL IS A DUALFREQUENCY RECEIVER RADIO NAVIGATION SYSTEM GLONASS, IN ORDER TO RECOGNIZE OBJECTS BASED ON THEIR RADAR PORTRAITS FOR MEDIA AND SYSTEM KKP

Proposals for constructing radio images of objects based on the synthesis of apertures are considered. The methods of radar recognition of space objects (QRs) are based on the deterministic model of the reflected signal. The deterministic model of the signal allows us to reproduce, without limitations, auxiliary signs used in the detection and recognition of targets (statistical parameters and generalized functions of signal realizations, etc.). It is necessary to obtain typical values of the parameters of the received signal for each typical structural element, and also to obtain a reliable signal reflected from the target for their comparison and classification. The necessary accuracy can only be achieved by collecting a large number of statistical information on the state of the radio path, the typical values of the signals for the reflecting elements of the structure, all this being compared and making a decision on the design of the QR. The processing of such a large amount of data can only be assigned with the help of neural networks. To increase the resolving power of measurements along the line of object movement (in azimuth), an artificial increase in the size of the antenna in the azimuth direction is used-the synthesis of the aperture. Synthesis of the aperture consists in the coherent accumulation of a series of signals reflected by the target while the object is in the antenna pattern. The range to the position of the radar relative to the moving object varies, as a result of which the phase of the reflected pulses changes. The coherent accumulation of reflected pulses during the formation of the synthesized aperture must take into account the indicated phase changes. The ionosphere is variable and the model does not always correctly describe its influence in a particular region, so monitoring the signals of the Glonass NS during radar experiments will increase the accuracy of measuring the coordinate information if the task of constructing radio images of the location objects is being posed. To obtain a high azimuth resolution, it is necessary to realize the so-called floodlight mode, when the antenna tracks the movement of the object, thereby increasing the time of its observation. Using ultra-wideband radar signals when working in floodlight mode allows to obtain a radar image with a resolution of up to 15 cm in range and 25 cm in azimuth.

UWB Radar Target Detection Based on Hidden Markov Models

In this paper, we propose ultra-wideband (UWB) radar target detection approach based on Hidden Markov Models (HMMs). HMMs are used as a classifier to identify signal with the presence of target in a background clutter and the pure clutter response signal. Time-frequency features are extracted and features have less correlation to each other are selected based on the feature covariance matrix and fed into HMMs. The detection experiments are conducted in two different scenarios: sense-through-foliage target detection and sense-through-wall human detection. The sense-through-foliage data set contains poor quality UWB radar return echoes using low amplitude transmitting pulses. Data collected from different radar locations are tested and detection results are presented. Sense-through-wall data are collected using different UWB radar and the target is human standing behind different types of walls. HMMs parameters are also investigated to optimally model UWB radar signals for target detection.

Non‐uniform sampling and super‐resolution method to increase the accuracy of tank gauging radar

Different methods are proposed for fluid level measurement in storage tanks. Majority of these methods have some in-contact elements or are based on mechanical waves which are vulnerable to changes due to temperature change, pressure change or ageing. Electromagnetic based (i.e. radar) level measurement is a contactless method and it is less vulnerable to such changes. However, a wide band or ultra-wide band radar signal should be transmitted in order to achieve the required accuracy. Usually, linear frequency modulation or step frequency waveforms are used for this goal and matched filter (MF) processing or fast Fourier transform (FFT) are used to find the true level of the liquid inside the tank. In this study, the authors have shown that, those super-resolution methods used for array signal processing and direction finding can be also used in tank gauging problem. Through the simulations and also practical laboratory test, it is shown that, if the frequency steps are selected non-uniformly, the super-resolution methods produce more accurate result compared with MF or FFT processing.

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.

Efficient and Robust RFI Extraction Via Sparse Recovery

This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since 1) RFI often occupies a wide range of the radar’s operating frequency spectrum; 2) RFI might have significant power; and 3) RFI signals are difficult to predict and model due to the nonstationary nature as well as the complexity of various communication devices. Our proposed framework involves an initial RFI estimation step that operates directly on already contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital prior information is then taken into account to construct an adaptive RFI dictionary with various sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we employ a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our method can be implemented as a denoising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks such as target detection and classification. Recovery results from extensive simulated data sets as well as real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.

A UWB Radar Signal Processing Platform for Real-Time Human Respiratory Feature Extraction Based on Four-Segment Linear Waveform Model.

This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

Target Localization with a Single Antenna via Directional Multipath Exploitation

Target localization in urban sensing can benefit from angle dependency of the pulse shape at a radar receiver antenna. We propose a localization approach that utilizes the embedded directivity in ultra-wideband (UWB) antennas to estimate target positions. A single radar unit sensing operation of indoor targets surrounded by interior walls is considered, where interior wall multipaths are exploited to provide target cross-range. This exploitation assumes resolvability of the multipath components, which is made possible by the virtue of using UWB radar signals. The proposed approach is most attractive when only few multipaths are detectable due to propagation obstructions or owing to low signal-to-noise ratios. Both simulated and experimental data are used to demonstrate the effectiveness of the proposed approach.

Statistical wavelet-based anomaly detection in big data with compressive sensing

Anomaly detection in big data is a key problem in the big data analytics domain. In this paper, the definitions of anomaly detection and big data were presented. Due to the sampling and storage burden and the inadequacy of privacy protection of anomaly detection based on uncompressed data, compressive sensing theory was introduced and used in the anomaly detection algorithm. The anomaly detection criterion based on wavelet packet transform and statistic process control theory was deduced. The proposed anomaly detection technique was used for through-wall human detection to demonstrate the effectiveness. The experiments for detecting humans behind a brick wall and gypsum based on ultra-wideband radar signal were carried out. The results showed that the proposed anomaly detection algorithm could effectively detect the existence of a human being through compressed signals and uncompressed data.

Target detection and classification by measuring and processing bistatic UWB radar signal

This paper presents a novel method to detect and classify targets obscured by foliage based on real data collected by a bistatic ultra-wideband (UWB) radar system. The type of the target which passes between the transmitter and receiver can have significant effects on the shape of the received waveform. The signal measured by the bistatic UWB radar is related to the type of the target. From these received signals, we extract features that are representative of the target types. Then, we develop target type classification and recognition algorithm based on machine learning techniques. An improved support vector machine (SVM) classifier is developed to perform target types classification and recognition. A novel chaotic differential evolution (CDE) optimization approach using tent map is adopted to determine the parameters of SVM. The effectiveness of the proposed approach is verified by experiments taken in the forest.

UWB Radar Signal Processing for Positioning of Persons Changing Their Motion Activity

In many applications of ultra-wideband (UWB) radar a character of target motion is a priori not known and naturally can differ within a group of multiple targets. Usually a signal processing aimed at the positioning of only moving persons or only static persons leads to a loss of information. To solve this task, the utilization of combined processing based on detection of non-stationary signal components in the time domain and human respiratory motions in the frequency domain is proposed in this paper. The results of such processing are more reliable and robust with respect to motion activity of all monitored persons, which is demonstrated by processing of measured UWB radar signals.

인체 움직임에 강인한 IR-UWB 레이더 기반의 호흡속도추정

본 논문에서는 IR-UWB 레이더 신호의 크기와 도달시간을 결합한 새로운 방법의 호흡속도추정 방법을 제안한다. 특히 이 논문에서는 분석을 통해서 인체의 흔들림이 호흡속도추정에 왜곡을 일으키지 않음을 증명한다. 분석을 바탕으로 레이더 신호의 크기 정보 신호와 도달시간 정보 신호의 컨볼루션 방법을 제안한다. 하드웨어 실험을 통한 분석을 통해서 호흡속도성분의 추출 능력이 기존의 추정 방법에 비해서 10dB 이상 향상됨을 보인다.

UWB Impulse Radar for Through-The-Wall Imaging

Ultra-Wideband (UWB) is the promising technology for localization of the objects behind the walls. Recent terrorist activities and law-enforcement situations underscore the need for effective through-wall detection. UWB radar signals has extremely large frequency spectrum and since low frequencies has more penetration capabilities through dielectric materials it is best suitable for Through-the-Wall Radar Imaging (TWRI). Signal processing in TWRI has a greater impact in getting the information of the scanned area. This paper uses impulse signals in TWRI, examines the factors impacting in TWRI and obtains the two dimensional information of the scanned scene. Electromagnetic simulation software is used to generate the room like structure, and to obtain the raw radar data.

Ultra-wideband Impulse-based Radar Signals for Through-the-wall Imaging

Ultra-wideband (UWB) is the promising technology for localization of the objects behind the walls. Recent terrorist activities and law-enforcement situations underscore the need for effective through-wall detection. The approval of UWB technology made by federal communications commission (FCC) in 2002 makes the researchers to have a look on this technology. UWB radar signals has extremely large frequency spectrum and since low frequencies has more penetration capabilities through dielectric materials it is best suitable for through-the-wall radar imaging (TWRI). Signal processing in TWRI has a greater impact in getting the information of the scanned area. This paper uses impulse signals in TWRI, examines the factors impacting in TWRI and obtains the two dimensional information of the scanned scene. Electromagnetic simulation software is used to generate the room like structure, and to obtain the raw radar data. Defence Science Journal, 2012, 62(1), pp.187-192 , DOI:http://dx.doi.org/10.14429/dsj.62.843

Unobtrusive Vital Sign Acquisition in the Domain of AAL

Addressing applications in the area of Ambient Assisted Living, this contribution presents a new kind of unobtrusive, and non-stigmatizing, continuous measurement of vital signs as respiratory rate by the observation and analysis of surrogating signals caused by interdependencies between ultra-wideband radar signals and altering target locations and coherent shapes e.g. the upheaval of the breast or contraction of a muscle. The key part of the approach is based on a mixture of techniques of pre-processing, feature extraction and information fusion including the context aware interpretation for the treatment of multidimensional reflection data sets from the UWB-sensor node. After a brief introduction to the application context including an explanation of specific user demands, physical fundamentals of measurement and utilized electronics, the applied principles of spatial and temporal data mining are described including meaningful experiments.