Direction-of-arrival (DoA) estimation is essential in many industrial internet of things (IIoT) applications where IoT users may require a high-resolution estimation algorithm for features limited to positioning and device localization. The conventional DoA estimation is challenging as the number of antenna arrays increases leading to an increase in design complexity and cost in RF circuitry. As a result, the conventional DoA estimation algorithms may not be suited for IIoT applications. In this work, a joint DOA and carrier frequency offset (CFO) schemes is introduced for typical uplink IIoT systems by equipping the receiver with a leaky-wave antenna (LWA). By considering that the LWA can simultaneously steer its beam direction towards the active user. As a result, each user is estimated independently. The joint algorithm is developed based on the high-resolution MUSIC scheme, which utilizes the LWA properties to compute the DoA spectrum. Furthermore, we develop an iterative CFO estimation approach as the desired solution to a multi-dimensional search. Subsequently, we further formulate the Cramer–Rao lower bounds (CRLB) to serve as a performance benchmark for the accuracy of the proposed scheme. The SDR platform is provided to validate the proposed algorithm in real-time. Finally, the simulation results and the SDR experiments justify that the proposed scheme offers more robustness than conventional counterparts with linear array antenna, which achieves a root mean square error (RMSE) approach to the derived CLRB, especially at a medium signal-to-noise ratio (SNR) range which suited for IIoT applications.

This paper proposes a near-field millimeter-wave frequency diverse sensing system based on a spoof surface plasmon polaritons (SSPP) transmission line. SSPP structure features high-dispersion field distribution along the line, greatly enhancing the sensing capacity within the specified frequency band. A probe-free retrieval approach is proposed to build the measurement matrix based on a small number of designed tests instead of probing the field distribution. Such a probe-free approach does not require experimentally probing field distribution which may bring errors to the measured field due to the perturbation of probes, and hence greatly improves the accuracy of the measurement matrix and, therefore, the sensing resolution. This is especially useful at millimeter-wave frequencies where probe perturbation is relatively strong in field measurement. As an illustrative example, we recovered a measurement matrix of 1001 spatial pixels by making only 20 tests using a known object at different known positions. The pixel resolution has reached λ/100. And using this measurement matrix, we are able to accurately sense the position of a copper strip with the sensing error of 0.1 mm (about λ/50).

This paper proposes a frequency-diverse multi-target wireless sensing system for integrated sensing and communication (ISAC) applications. A coupled-patch antenna (CPA) with frequency-dependent beam scanning capability is employed as the key component for multi-target localization and respiration detection. Owing to the high scanning rate of $25.8^{\circ }$ per $1\%$ bandwidth, the designed CPA is able to continuously scan an angle range of $93^{\circ }$ across broadside as frequency varies from 3.26 GHz to 3.38 GHz, which well fits the ISAC application in Sub-6 GHz frequency. Four examples are given to demonstrate the benefit of high-scanning-rate CPA for multi-target localization, respiration detection, and surrounding interference mitigation.

The diversity of the field pattern in a radiation aperture plays a pivotal role on the capacity enhancement of microwave imaging and sensing. Conventional methodologies typically employ multiple ports to excite a 2-D aperture for frequency diversity enhancement. However, multi-port excitation poses significant challenges for transceivers when MIMO technology is implemented, while employing switches can result in slower imaging speed. This paper presents a single-port 2D phase coding planar meta-antenna that generates frequency diverse radiation fields, aiming to achieve single-shot highspeed microwave imaging. A prototype of meta-antenna with 13.34λ0 × 6.67λ0 aperture size is designed and fabricated for microwave imaging within 10-15 GHz. It achieves a spatial resolution of 5 mm in y-direction and 9 mm in x-direction for experimental demonstration, which finally validates the proposed diversity-enhanced meta-antenna. The proposed meta-antenna may find wide applications in integrated communication and sensing.

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.

Hypertension (HPT) is a chronic disease characterized by the consistent elevation of arterial blood pressure, which is considered to be a significant risk factor for conditions such as stroke, coronary artery disease, and heart failure. The detection and continuous monitoring of HPT can be a demanding process. As a non-contact measuring method, the ballistocardiography (BCG) signal characterizes the repetitive body motion resulting from the forceful ejection of blood into the major blood vessels during each heartbeat. Therefore, it can be applied for HPT detection. HPT detection with BCG signals remains a challenging task. In this study, we propose an end-to-end deep convolutional model BH-Net for HPT detection through BCG signals. We also propose a data augmentation scheme by selecting the J-peak neighborhoods from the BCG time sequences for hypertension detection. Rigorously evaluated via a public data-set, we report an average accuracy of 97.93% and an average F1-score of 97.62%, outperforming the comparative state-of-the-art methods. We also report that the performance of the traditional machine learning methods and the comparative deep learning models was improved with the proposed data augmentation scheme.