Ultra-wideband Radar Technology Research Articles

Gender classification based on gait analysis using ultrawide band radar augmented with artificial intelligence

The identification of individuals based on their walking patterns, also known as gait recognition, has garnered considerable interest as a biometric trait. The use of gait patterns for gender classification has emerged as a significant research domain with diverse applications across multiple fields. The present investigation centers on the classification of gender based on gait utilizing data from Ultra-wide band radar. A total of 181 participants were included in the study, and data was gathered using Ultra-wide band radar technology. This study investigates various preprocessing techniques, feature extraction methods, and dimensionality reduction approaches to efficiently process Ultra-wide band radar data. The data quality is improved through the utilization of a two-pulse canceller and discrete wavelet transform. The hybrid feature dataset is generated through the creation of gray-level co-occurrence matrices and subsequent extraction of statistical features. Principal Component Analysis is utilized for dimensionality reduction, and prediction probabilities are incorporated as features for classification optimization. The present study employs k-fold cross-validation to train and assess machine learning classifiers, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, K-Nearest Neighbors, and Extra Tree Classifier. The Multilayer Perceptron exhibits superior performance, achieving an accuracy of 0.936. The Support Vector Machine and k-Nearest Neighbors classifiers closely trail behind, both achieving an accuracy of 0.934. This research is of the utmost importance due to its capacity to offer solutions to crucial problems in multiple domains. The findings indicate that the utilization of UWB radar data for gait-based gender classification holds promise in diverse domains, including biometrics, surveillance, and healthcare. The present study makes a valuable contribution to the progress of gender classification systems that rely on gait patterns.

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach.

Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.

Validation of a New Contactless and Continuous Respiratory Rate Monitoring Device Based on Ultra-Wideband Radar Technology.

Respiratory rate (RR) is typically the first vital sign to change when a patient decompensates. Despite this, RR is often monitored infrequently and inaccurately. The Circadia Contactless Breathing Monitor™ (model C100) is a novel device that uses ultra-wideband radar to monitor RR continuously and un-obtrusively. Performance of the Circadia Monitor was assessed by direct comparison to manually scored reference data. Data were collected across a range of clinical and non-clinical settings, considering a broad range of user characteristics and use cases, in a total of 50 subjects. Bland–Altman analysis showed high agreement with the gold standard reference for all study data, and agreement fell within the predefined acceptance criteria of ±5 breaths per minute (BrPM). The 95% limits of agreement were −3.0 to 1.3 BrPM for a nonprobability sample of subjects while awake, −2.3 to 1.7 BrPM for a clinical sample of subjects while asleep, and −1.2 to 0.7 BrPM for a sample of healthy subjects while asleep. Accuracy rate, using an error margin of ±2 BrPM, was found to be 90% or higher. Results demonstrate that the Circadia Monitor can effectively and efficiently be used for accurate spot measurements and continuous bedside monitoring of RR in low acuity settings, such as the nursing home or hospital ward, or for remote patient monitoring.

Monitoring Activities of Daily Living Using UWB Radar Technology: A Contactless Approach

In recent years, the ultra-wideband (UWB) radar technology has shown great potential in monitoring activities of daily living (ADLs) for smart homes. In this paper, we investigate the significance of using non-wearable UWB sensors for developing non-intrusive, unobtrusive, and privacy-preserving monitoring of elderly ADLs. A controlled experiment was setup, implementing multiple non-wearable sensors in a smart home Lab setting. A total of nine (n = 9) participants were involved in conducting predefined scenarios of ADLs- cooking, eating, resting, sleeping and mobility. We employed the UWB sensing prototype and conventional implementation technologies, and the sensed data of both systems were stored, analysed and their performances were compared. The result shows that the performance of the non-wearable UWB technology is as good as that of the conventional ones. Furthermore, we provided a proof-of-concept solution for the real-time detection of abnormal behaviour based on excessive activity levels, and a model for automatic alerts to caregivers for timely medical assistance on-demand.

A Pilot Study of Impulse Radio Ultra Wideband Radar Technology as a New Tool for Sleep Assessment.

To validate Impulse radio ultra wideband pulse-doppler radar technology against polysomnography (PSG) for sleep assessment. In all, 12 participants were recruited and their overnight sleep was assessed both by a Novelda XeThru radar and PSG. Two subjects had two nightly recordings, whereas 10 had one recording. Epoch by epoch (30 seconds) comparisons from bedtime to rise time were conducted. Concordance was estimated in terms of the mean difference between the radar and the PSG estimates regarding sleep onset latency, wake time after sleep onset and total sleep time. In addition, accuracy, sensitivity, specificity and Cohen kappa were calculated. The mean difference (minutes) between the radar and the PSG registrations was -5.7 minutes (standard deviation [SD] = 22.1 minutes) for sleep onset latency, 6.4 minutes (SD = 32.5 minutes) for wake after sleep onset, and 1.5 minutes (SD = 24.6 minutes) for total sleep time. The mean values obtained for accuracy, sensitivity, specificity and Cohen kappa were 0.931, 0.961, 0.695 and 0.670, respectively. Impulse radio ultra wideband radar technology is a promising tool in terms of affordable and practical objective sleep assessment. Further technical development and more validation studies are needed in order to conclude about the utility potential of this device.

Ultra-wideband imaging radar to reveal obstacles concealed in vegetation to improve navigation of unmanned ground vehicles

To increase mobility of an unmanned ground vehicle (UGV), this study describes the use of forward-looking imaging radar applying ultra-wideband radar technology. If radar is able to visualize images with high resolution in front of a UGV, the radar can be used to support the vehicle by detecting obstacles through foliage. This system employs the multi-input/double-output configuration with two transmitting horn antennas to increase cross-range resolution. Also, two rows of physical receiving antenna arrays provide the height information of targets using an interferometer principle. For visualizing objects in front of the radar, the back-projection method is applied to the data acquired from the receivers. A field test is used to verify the effectiveness of the new radar system. The performance of the radar is proved experimentally by the analysis of image resolution, grass-penetrating capability, and target height information.

A novel sense-through-foliage target recognition system based on sparse representation and improved particle swarm optimization-based support vector machine

A new recognition system of improved particle swarm optimization-based support vector machine (SVM) combined with sparse representation-based feature extraction is proposed for recognize targets obscured by foliage. Real data sets of four kinds of samples are acquired using a bistatic ultra-wideband (UWB) radar system. Sparse representation (SR) theory is applied to analyzing the components of received target echo signals and sparse coefficients are used to describe target features, the dimension of the sparse coefficients is reduced using principal component analysis (PCA). Support vector machine is a powerful tool for solving the recognition problem with small sampling, nonlinearity and high dimension. Improved particle swarm optimization (IPSO) is developed in this study to determine the optimal parameters for SVM with the highest accuracy and generalization ability. The experiment results indicate that the method of feature extraction using SR can effectively represent the original data better. The recognition result of the proposed method is also compared with SVM, k-nearest neighbor (KNN) and BP neural network (BPNN). The effectiveness of the proposed approach is verified by experiments taken in the forest environment. Our findings show that the proposed method combined with bistatic UWB radar technology provides a good access to achieve the aim of automatic sense through foliage target recognition.

Ultrawideband (UWB) Radar Imaging of Building Interior: Measurements and Predictions

In this paper, we explore using the ultrawideband radar technology for sensing through-the-wall imaging. We conducted a field experiment with our in-house designed impulse-based radar, in which a stripmap synthetic aperture radar (SAR) geometry was employed in mapping an abandoned army barrack building with a large footprint area. The images obtained from two sides of the building were combined in order to obtain the full building layout, including the interior structure. Computer simulations performed with Xpatch, a physical optics-based ray tracing code, are used to explain some phenomena observed in the measured SAR images.

Characterization of wall dispersive and attenuative effects on UWB radar signals

Abstract This paper addresses the potentials of ultra-wideband (UWB) through-wall imaging radars compared with conventional narrowband systems. The challenges that limit the utilization of high precision UWB systems are examined with the aim of mitigating them. These challenges include multi-path, pulse dispersion, and antenna effects on the pulse shape due to angles of transmission and arrival. The propagation of UWB signals through walls is a crucial factor in determining the success of UWB radar technology. UWB signals, when propagating through walls, not only suffer attenuation but also distortion due to dispersive properties of the walls. This paper examines time- and frequency-domain techniques for measuring the electromagnetic properties of construction materials in the UWB frequency range. The measured parameters provide valuable insights in appreciating the capabilities and limitations of the UWB technology. Special attention is paid to time gating as a mean to extract the direct-path signal from the multi-path components. Both single-pass and multi-pass models are discussed. Multi-pass models account for the multiple reflections within the wall while the single-pass model assumes the possibility of gating out a single transmission. The partition-dependent narrowband propagation model is modified to account for the ultra-wide bandwidth of the signal. The paper illustrates the application of the modified model in indoor environments. The modified model is helpful in estimating the link power budget. It is also useful in studying the performance of UWB systems for indoor communication and positioning applications.

UWB Radar System for Yield Monitoring of Sugar Beet

The objective of this research was to develop a yield monitoring system primarily for sugar beets by applying ultra-wideband (UWB) radar technology. Like radar systems used in geological engineering, these devices acquire data via electromagnetic waves. The basic idea was to use such data to identify sugar beets in agricultural soils, which would provide non-invasive in-soil detection of sugar beets in order to enable counting of individual sugar beets and determination of single sugar beet root mass. Further goals were to enhance the methods and procedures of the UWB radar technology for other applications in agriculture, as a general goal, and to define applicability restrictions of the system for sugar beet and other root crops. In the first phase of the research, laboratory experiments were used to develop, test, and consequently improve the data acquisition and processing procedures. In this first phase, the feasibility of detecting sugar beets in soil in laboratory conditions using the reflected energy with a simple threshold approach was confirmed. In the second phase, the potential detectability and size estimation of sugar beets based on the distribution and consistency of backscattered energy amounts obtained in field conditions was researched. Based on the data recorded in field conditions, visual detectability was above 90%, with good correlations between sugar beet positions and energy peak positions. The best correlations between mass of sugar beets and the amounts of backscattered energy exceeded 80%.