Impulse Radio Ultra-wideband Research Articles

Enhanced Vital Parameter Estimation Using Short-Range Radars with Advanced Motion Compensation and Super-Resolution Techniques

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RRs and CRs even in the presence of body movement.

Lightweight Multi-Domain Fusion Model for Through-Wall Human Activity Recognition Using IR-UWB Radar

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%.

Feasibility of Early Assessment for Psychological Distress: HRV-Based Evaluation Using IR-UWB Radar.

Mental distress-induced imbalances in autonomic nervous system activities adversely affect the electrical stability of the cardiac system, with heart rate variability (HRV) identified as a related indicator. Traditional HRV measurements use electrocardiography (ECG), but impulse radio ultra-wideband (IR-UWB) radar has shown potential in HRV measurement, although it is rarely applied to psychological studies. This study aimed to assess early high levels of mental distress using HRV indices obtained using radar through modified signal processing tailored to reduce phase noise and improve positional accuracy. We conducted 120 evaluations on 15 office workers from a software startup, with each 5 min evaluation using both radar and ECG. Visual analog scale (VAS) scores were collected to assess mental distress, with evaluations scoring 7.5 or higher classified as high-mental distress group, while the remainder formed the control group. Evaluations indicating high levels of mental distress showed significantly lower HRV compared to the control group, with radar-derived indices correlating strongly with ECG results. The radar-based analysis demonstrated a significant ability to differentiate high mental distress, supported by receiver operating characteristic (ROC) analysis. These findings suggest that IR-UWB radar could be a supportive tool for distinguishing high levels of mental stress, offering clinicians complementary diagnostic insights.

Self-attention CNN based indoor human events detection with UWB radar

In the era of smart homes and healthcare automation, the ability to accurately monitor and detect indoor human activities is paramount. Ultra-wideband (UWB) radar has emerged as a promising means for event detection, given its non-invasive nature and easy deployment in diverse environments. However, despite the advances in radar-based event detection, challenges remain, such as distinguishing between similar events like falls and rapid sitting. To address these challenges, for the first time, we propose an impulse radio-ultrawideband (IR-UWB) radar system to collect over ten thousand radar echo signals of eight similar actions from different angles and design a self-attention-based low-complexity convolutional neural network (CNN) model for event classification. The model leverages global correlations in radar signal spectrograms to efficiently extract features. A comparative simulation study is conducted to evaluate the detection accuracy of the proposed model and some of the existing methods based on different dataset sizes and CNN configurations. Moreover, the influence of different self-attention structures on precision and model parameter count is analyzed. Our findings reveal that the proposed self-attention-based CNN model significantly outperforms other traditional machine learning techniques while maintaining a low level computational complexity.

High data rate and energy efficient configurable IR-UWB transmitter with an integrated balun

This paper presents a novel, digitally configurable, switching oscillator-based impulse radio ultra-wideband (IR-UWB) transmitter that is robust to process variations. The pulse width and pulse position properties of the output waveform are configurable to ensure operation for the desired bandwidth and output pulse energy values. The architecture is based on a cross-coupled LC voltage-controlled oscillator (LC VCO) implemented with a custom-designed on-chip balun at a center frequency of 2.4 GHz. With the usage of this on-chip balun, the need for an extra buffer stage is eliminated, and the highest possible efficiency is achieved with the proper turns ratio selected. The generated digital control pulse signals are also used to ensure fast start-up and UWB operation while maintaining low power consumption with an implemented on/off tail switching scheme. The presented IR-UWB transmitter that occupies a total area of 0.35 mm2 is fabricated in the United Microelectronics Corporation (UMC) 180-nm mixed-mode/radio frequency (RF) CMOS process. The measured output voltage swing of the fabricated transmitter is 0.812 V on a 50-Ω load for the 200 Mb/s data rate, and the bandwidth is measured as 902 MHz. The achieved frequency tuning range is 1 GHz and the fabricated transmitter can drive 50 Ω, 75 Ω and 100 Ω loads with measured efficiency values 4.16%, 5.76%, and 4.05%, respectively. Compared to other works in the literature, this design achieves a higher output pulse energy at a higher data rate while maintaining low power consumption and, thus, high efficiency.

A 0.5–15 GHz reconfigurable IR-UWB receiver in 28 nm CMOS

A 0.5–15 GHz ultra-wideband (UWB) RF reconfigurable receiver in UMC 28 nm CMOS used for IR-UWB (impulse radio UWB) radar system is proposed in this paper. This receiver employs a 0.5–8 GHz path and an 8–15 GHz path to achieve the wide frequency band, and we proposed an RF channel select voltage-to-time converter (VTC) circuit. Two different low noise amplifiers (LNAs) are adopted in the two paths to obtain the high performance of noise figure (NF) and gain. The common drain stage and resistance negative feedback technology as well as the source degeneration inductive technology are introduced to achieve a high flat gain and fine input matching. The R-2R variable load network of a programable gain amplifier (PGA) gives it a nearly 24 dB dynamic range and approximately linear gain control. A 500MS/s time domain analog to digital converter (TD-ADC) with an equivalent sampling rate of 16GS/s is shared for the two paths which can realize the direct RF sampling. The input RF signal path selection is implemented by configuring the Band_sel signal. The receiver has reconfigurability of the RF front end and better power consumption and area. In the frequency of 0.5–8 GHz, the S11 is below −7.9 dB and the NF is smaller than 6 dB. In the frequency of 8–15 GHz, the S11 is below −8dB and the NF is smaller than 11 dB. The receiver dissipates 242.72mW with an area of 1.01 mm2

A 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces.

This paper proposes a low-power, high-speed impulse radio-ultra-wideband (IR-UWB) transceiver for brain computer interfaces (BCIs) using amplitude and synchronized time shift keying technique (ASTSK). The proposed IR-UWB transmitter (Tx) generates two pulses (sync pulse and data pulse) per symbol rate. The time difference between two pulses is used for synchronized time shift keying and the amplitude of the two pulses is used for amplitude shift keying. The receiver (Rx) demodulates the time difference with a low power time-to-digital converter (TDC) and peak detector (PD) based amplitude demodulation is suggested to relax analog-to-digital converter (ADC) burden for low power receiver. Especially the Tx-based synchronized operation eliminates the need for complex clock circuitry such as phase-lock loop (PLL) and reference crystal oscillator. Therefore, it can achieve low power and high-speed operation. The prototype, fabricated in 65 nm CMOS, has a frequency range of 6-9 GHz, communication speed of 1.28 Gbps, and power consumption of 18 mW (Tx) and 58 mW (Rx). This work is a fully integrated RF transceiver adapted for high-order modulation and designed to include the receiver.

78.8 pJ/b, 100 Mb/s Noncoherent IR-UWB Receiver for Multichannel Neurorecording Implants.

In this article, we present a novel approach for designing a low-power, low-area impulse radio ultra-wideband (IR-UWB) noncoherent receiver capable of achieving a data rate of 100 Mbps. Our proposed receiver demonstrates the ability to demodulate ON-OFF keying pulse streams across the entire lower frequency band defined by the Federal Communication Commission for UWB applications. The key components of the proposed receiver include a reconfigurable differential two-stage low-noise amplifier, a fully differential squarer, narrow-band interface rejection filters, and variable gain baseband amplifiers. These circuits work cohesively to ensure efficient signal reception and processing. To validate the performance of the proposed receiver, we implemented the design using TSMC 40-nm CMOS process technology. A short-range communication including a 1.5 cm tissue layer is tested utilizing a typical upconversion UWB transmitter fabricated in the same technology. Remarkably, the proposed receiver achieves a data rate of 100 Mbps with an impressively low energy efficiency of 78.8 pJ/b and occupies an area of 0.705 mm2. The compact size, remarkable energy efficiency, and high data rate capabilities of the proposed receiver meet the stringent requirements of neural recording implants.

A Hybrid Approach to Impulse-Radio UWB Self-Localization with Passive Reflectors

A Hybrid Approach to Impulse-Radio UWB Self-Localization with Passive Reflectors

基于超宽带冲激雷达的人体动作识别和心率估计数据集

Human action recognition and heart rate estimation for indoor human targets carry significant applications in many fields such as smart homes, health care, sports analysis, etc., and have attracted increasing attention in recent years. Impulse-Radio Ultra-Wideband (IR-UWB) radar, with its advantages in privacy protection, environmental requirements and performance, has found extensive applications in indoor human target monitoring. In view of the data demand for indoor human perception related research based on IR-UWB radar, and the issues with a lack of relevant public data and inadequacy in the authenticity of simulation data, this paper presents a dataset of human action recognition and heart rate estimation based on IR-UWB radar through typical indoor scene measurement experiments and data processing and annotation. The dataset collects 1,629 groups of action recognition data (i.e. unmanned scene data and 10 common actions such as walking, waving, squatting, etc.) from 10 human targets as well as 4,670 groups of heart rate estimation data. All data are presented in a standardized format with precise labeling, making it a valuable resource for research in relevant fields.

Design of a multi-standard IR-UWB emitter in a 28 nm FD-SOI technology based on the frequency transposition pulse synthesis

Ultra-WideBand Impulse Radio (IR-UWB) is a wireless communication technology well-suited for short range communications (about 10 meters) and has the advantage to allow real-time location with a precision of 5 cm. However, its use has been standardized in some standards and especially in the IEEE 802.15.4 dedicated to Wireless Personnal Area Network (WPAN) and in the IEEE 802.15.6 dedicated to Wireless Body Area Network (WBAN). After presenting requirements of each standard and a literature survey on the IR-UWB emitter, the design of a multi-standard IR-emitter based on the frequency transposition pulse synthesis is proposed in a 28 nm FD-SOI technology. The proposed design has been validated at the transistor level using electrical simulations which take into account the parasitic capacitance at each critical node of the circuit. Moreover, results show that the proposed circuit has the potential to be the first multi-standard IR-UWB emitter which covers every channel of the IEEE 802.15.4 and 802.15.6 standards.

Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning

<span lang="EN-US">Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%.</span>

Design and Implementation of a Transmitter for IR-UWB Standard

In wireless communication, the Ultrawide Band (UWB) is a technique for achieving a higher data rate, low power consumption, and less complexity. Impulse Radio - Ultra-Wide Band (IR-UWB) uses the baseband signal technique, reducing circuit complexity and power consumption. This work proposes an IR-UWB transmitter block with low power and tunable bandwidth that meets the UWB regulations of LRP (Low-Rate Pulse) UWB. The transmitter proposed uses a time-interleaved architecture using 0.18um CMOS technology using Cadence Virtuoso, which consists of On Off Keying (OOK) Circuit for the four stages of a pulse generator that operates in 6.17GHz-9.10GHz. The width of the pulse is between 0.096ns to 0.162ns using a current steering inverter. The proposed transmitter block has an output swing of 30 mV at 1MHz input Pulse Repetition Frequency (PRF) and a maximum PRF of 1GHz, at a 1GHz pulse rate, the power consumption was measured to be 8.3 mW.

Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures

Developing accurate classification models for radar-based Human Activity Recognition (HAR), capable of solving real-world problems, depends heavily on the amount of available data. In this paper, we propose a simple, effective, and generalizable data augmentation strategy along with preprocessing for micro-Doppler signatures to enhance recognition performance. By leveraging the decomposition properties of the Discrete Wavelet Transform (DWT), new samples are generated with distinct characteristics that do not overlap with those of the original samples. The micro-Doppler signatures are projected onto the DWT space for the decomposition process using the Haar wavelet. The returned decomposition components are used in different configurations to generate new data. Three new samples are obtained from a single spectrogram, which increases the amount of training data without creating duplicates. Next, the augmented samples are processed using the Sobel filter. This step allows each sample to be expanded into three representations, including the gradient in the x-direction (Dx), y-direction (Dy), and both x- and y-directions (Dxy). These representations are used as input for training a three-input convolutional neural network-long short-term memory support vector machine (CNN-LSTM-SVM) model. We have assessed the feasibility of our solution by evaluating it on three datasets containing micro-Doppler signatures of human activities, including Frequency Modulated Continuous Wave (FMCW) 77 GHz, FMCW 24 GHz, and Impulse Radio Ultra-Wide Band (IR-UWB) 10 GHz datasets. Several experiments have been carried out to evaluate the model's performance with the inclusion of additional samples. The model was trained from scratch only on the augmented samples and tested on the original samples. Our augmentation approach has been thoroughly evaluated using various metrics, including accuracy, precision, recall, and F1-score. The results demonstrate a substantial improvement in the recognition rate and effectively alleviate the overfitting effect. Accuracies of 96.47%, 94.27%, and 98.18% are obtained for the FMCW 77 GHz, FMCW 24 GHz, and IR- UWB 10 GHz datasets, respectively. The findings of the study demonstrate the utility of DWT to enrich micro-Doppler training samples to improve HAR performance. Furthermore, the processing step was found to be efficient in enhancing the classification accuracy, achieving 96.78%, 96.32%, and 100% for the FMCW 77 GHz, FMCW 24 GHz, and IR-UWB 10 GHz datasets, respectively.

An Excess Kurtosis People Counting System Based on 1DCNN-LSTM Using Impulse Radio Ultra-Wide Band Radar Signals

As the Artificial Intelligence of Things (AIOT) and ubiquitous sensing technologies have been leaping forward, numerous scholars have placed a greater focus on the use of Impulse Radio Ultra-Wide Band (IR-UWB) radar signals for Region of Interest (ROI) population estimation. To address the problem concerning the fact that existing algorithms or models cannot accurately detect the number of people counted in ROI from low signal-to-noise ratio (SNR) received signals, an effective 1DCNN-LSTM model was proposed in this study to accurately detect the number of targets even in low-SNR environments with considerable people. First, human-induced excess kurtosis was detected by setting a threshold using the optimized CLEAN algorithm. Next, the preprocessed IR-UWB radar signal pulses were bundled into frames, and the resulting peaks were grouped to develop feature vectors. Subsequently, the sample set was trained based on the 1DCNN-LSTM algorithm neural network structure. In this study, the IR-UWB radar signal data were acquired from different real environments with different numbers of subjects (0–10). As indicated by the experimental results, the average accuracy of the proposed 1DCNN-LSTM model for the recognition of people counting reached 86.66% at ROI. In general, a high-accuracy, low-complexity, and high-robustness solution in IR-UWB radar people counting was presented in this study.

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting.

Adaptive LDPC coded high data rate based IR-UWB combined with VMIMO system for vehicular communication links

The automotive industry is developing significantly with the evolution of vehicular networks, intelligent transportation systems, and autonomous cars. These technologies are made achievable by recent advances in software, hardware, and communication systems, along with various applications and standardization efforts. On the other side, many impairments have existed due to these evolving, limiting the process of vehicular networks. So, this paper proposes an implementation of adaptive LDPC-coded, high data rate-based Impulse Radio-Ultra Wide Band (IR-UWB) combined with Virtual Multiple Input Multiple Output (VMIMO) systems for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Furthermore, they are merged with orthogonal pulse shaping using a modified Hermite pulse. Besides, the paper compares Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detection techniques. To enhance and mitigate the existing impairments facing the vehicular networks. The proposed system’s performance is evaluated using both techniques, utilizing Bit Error Rate (BER), processing time, and resultant throughput analysis for both channel models. The simulation results include Non-Line of Sight (NLoS) and Line of Sight (LoS) scenarios for both channel models. Moreover, the simulation considers the effect of path loss and noise on the transmitted signal. In addition, the recent LDPC decoding algorithm termed “Modified Implementation Efficient Reliability Ratio Weighted Bit Flipping” (MIERRWBF) is evaluated through all latter channel models. At last, a novel adaptive LDPC decoder is proposed to further enhance the performance of all vehicular communication links under study. Consequently, the paper proposed an integrated adaptive LDPC-coded high data rate communication system to enhance vehicular communications using VMIMO mixed with modified Hermite orthogonal pulse shaping and deploying the MMSE recommended detection technique.

Broadband bowtie antenna for short range impulse radar applications

In this paper, the resistively loaded bowtie antenna (RBA) is characterized in both frequency and time domains for short pulse radiating and sensing impulse radio ultra-wide band applications. The exclusive design procedures of RBA are implemented, aiming to optimize RBA to be suitable for short range target sensing applications. The figure of merit is introduced to reflect the short pulse radiating characteristic into the design procedures. The developed RBA is comprehensively evaluated by additional proposed standard criteria such as evaluation factor (EF) and pulse distortion factor (DF). Those EF and DF for RBA are calculated as 149 and 0.12, respectively. The performances of RBA are remarkably investigated in terms of short-range impulse radar characteristic perspective. The proposed RBA has been experimentally demonstrated by intensive characteristic investigations, which are to be commensurate with short range impulse radar applications.

An OOK and Binary FSK Reconfigurable Dual-Band Noncoherent IR-UWB Receiver Supporting Ternary Signaling

This article presents a multiband low-power and low-complexity impulse radio ultrawideband (IR-UWB) noncoherent receiver. The proposed receiver can be digitally reconfigured in three different modes of operation, including two single-band modes and one concurrent dual-band mode. In the two single-band modes, the proposed envelope detection architecture is capable of receiving and demodulating an on–off keying (OOK) pulse stream at RF center frequencies of 2.8 or 4.8 GHz. In the concurrent dual-band mode, the proposed architecture is able to demodulate binary frequency-shift keying (FSK), in addition to OOK demodulation, at center frequencies of 3 and 5 GHz. The receiver is composed of a reconfigurable low-power differential low noise amplifier (LNA), a fully differential squarer (self-mixer circuit), low-pass filter (LPF), and variable gain baseband (BB) amplifiers. The receiver is fabricated in TSMC 130-nm CMOS process technology. The receiver can operate at up to 150 Mb/s with the ternary signaling that is enabled by the binary FSK modulation combined with the OOK modulation in concurrent dual-band mode. At its maximum gain, the receiver achieves a sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 72 dBm at a bit error rate (BER) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-3}$</tex-math> </inline-formula> at a 100-Mb/s data rate. It consumes 11.9 and 13.2 mW from a 1.2-V supply in the single-band modes and concurrent dual-band mode, respectively.

Machine Learning-Based Joint Vital Signs and Occupancy Detection With IR-UWB Sensor

This article proposes the machine learning (ML)-based joint vital signs (VSs) and occupancy detection (OD) with an impulse radio ultra-wideband (IR-UWB) sensor. Works that have been done on VS or OD development using an IR-UWB are related to how VS works. In the related experiments performed, the OD and state of individuals were not sufficiently verified, and the methods were computationally complex. Issues related to the use of ML for joint VS and OD (VSOD) have also not been studied in the literature. Extensive experimental scenarios involving the application of an ML-based classifier for human OD and VS classification, which we extended toward three sub-scenarios, were evaluated. We formulated a solution for VS estimation, which was aligned, so that each network input sequence received signal corresponding to respective VS over different scenarios. The performance of the proposal was evaluated with other competing ML-based classification algorithms. Compared with other techniques, our proposed deep neural network (DNN)-based classifier achieved the best results, and it also offers benefits over other algorithms, such as not needing to extract features from the data.