A full understanding of normal motion from human limb joint trajectory tracking could be essential to develop and establish a scientific basis for correcting any abnormalities. Practicality of using continuous wave (CW) radars, being simple and low cost, has been successfully investigated for non-contact gait monitoring, but it is still challenging to identify the motions of different limb joints. Here, we investigate the feasibility of extending the 1-D block processing algorithm to distinctly track specific limb joints and discuss the advantages and limitations of the technique for CW radar. To establish a repeatable reference data, we run a full wave EM analysis on customized Boulic human model emulating specific video recorded body motions. Results based on measured data are in agreement with simulated ones for simple motions like swinging one hand or one leg only. The proposed technique is also successful in extracting lower body parts while the whole body is in motion, however it is still hard to clearly extract upper body parts like swinging hands.

In this article, a channel micro-Doppler ( $\mu \text{D}$ ) signatures’ quantitative evaluation method for multiple-input–multiple-output (MIMO) radar system, named relative signal–noise ratio in the time–frequency domain and denoted as RSNRtf, is introduced. The impact on RSNR _tf by some key dominant factors in real practical and diverse detection environments was theoretically analyzed first. Then, this proposed method is exploited to conduct effective and superior channel selection for improved human activity classification at random human target positions. Based on numerous MIMO stepped-frequency continuous wave (SFCW) radar data from eight subjects with seven activities at nine positions in a through-wall within a view angle of ±20° and up to 6-m distance away, the influence of various primary factors on the proposed method is investigated. Investigative experiments showed that the proposed method has a wide range of applicability and a high interference tolerance even for different detection distances, angles, activities, and subjects. Classification experimental results based on typical classifiers also showed that the proposed method could effectively improve human activity classification. Moreover, we can claim that the proposed method can achieve a higher outstanding performance in the channel $\mu \text{D}$ capacity evaluation and enhanced classification for the MIMO-radar-based human activity classification in free space or with a wider detection field with large angle range.

This study presents mm-MuRe, a novel method to perform multi-subject contactless respiration waveform monitoring by processing raw multiple-input-multiple-output mmWave radar data with an end-to-end deep neural network. The traditional vital signs monitoring signal processing scheme for mmWave radar involves analog or digital beamforming, human subject localization, phase variation extraction, filtering, and rate or biomarker analysis. This traditional method has many downsides, including sensitivity to selected beamforming weights and over-reliance on phase variation. To avoid these drawbacks, mm-MuRe (for MM-wave based MUlti-subject REspiration monitoring) is developed to improve reconstruction accuracy and reliability by taking in unprocessed 60 GHz MIMO FMCW radar data and outputting respiratory waveforms of interest, effectively mimicking an adaptive beamformer and bypassing the need for traditional localization and vital signs extraction techniques. Extensive testing across scenarios differing in range, angle, environment, and subject count demonstrates the network's robust performance, with an average cosine similarity exceeding 0.95. Results are compared to two baseline methods and show more than a 10% average improvement in waveform reconstruction accuracy across single and multi-subject scenarios. Coupled with a rapid inference time of 8.57 ms on a 10 s window of data, mm-MuRe shows promise for potential deployment to efficient and accurate near-real-time contactless respiration monitoring systems.

The demand for progressively higher microwave power levels is growing fast. In the past, traveling-wave tubes (TWTs) played a key role in constructing a wide range of microwave systems for various applications, such as radars, satellites, and wireless communication links. However, their poor resilience to degradation significantly affects system reliability. Meanwhile, TWT amplifiers (TWTAs) are expensive, bulky, and require a considerable warm-up time and high-dc voltage levels, especially in low-frequency bands [1] , [2] .

Design equations have been developed for the design of an H-plane rectangular waveguide T-junction with an arbitrary power split. These design equations were derived using curve fitting of data produced from a large number of EM simulation runs. A design procedure has been successfully developed and resulted in scalable, practically manufacturable structures. Various types of discontinuities were judiciously placed in the junction to provide adequate degrees of freedom and concurrently achieve diverse design goals. Added discontinuities included wedges and diaphragms which have been implemented as an integral part of the prescribed structures. For demonstration, T-junction designs at Ku-band have been fabricated and tested, which have adequate bandwidth performance covering the US-DBS 12.2–2.7 GHz frequency range. Both excellent match at the input port and in-phase flat power-split ratio over a relatively wide band at the output ports were successfully achieved—an essential requirement for high-performance antenna feeds. The dimensional tolerance of the developed design was also thoroughly investigated using extensive sensitivity analysis, and the developed structures proved to be appropriate for low-cost high-volume fabrication methods such as casting. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.

The common middle point (CMP) ground penetrating radar (GPR) utilizes the time-delay information under different antenna separations to realize layered inversion. However, parameter inversion of multilayered media is mostly subject to heavy computational complexity and worst estimation error in the determination of refraction positions. To address the problems, an effective parameter inversion method is proposed based on off-grid sparse CMP model with refined orthogonal matching pursuit (OMP) for multilayered parameter inversion. In the proposed method, a sparse CMP signal model with accurate reflected wave propagation modeling is developed based on a refraction approximation method. An off-grid sparse CMP model is further constructed based on second-order Taylor expansion to overcome the deviation from the grid node. Then, a refined OMP algorithm based on compressed sensing (CS) is proposed with an off-grid optimization process to achieve accurate off-grid parameter inversion. Finally, the effectiveness of the proposed method is verified by simulations and experiments.