Radar Signal Research Articles

A Method for Classification of Chest Motion base on Time-domain Signals of IR-UWB Radar

Abstract As an instrument for wireless sensing and measurement, radar is widely used in target tracking and localization. And Impulse Radio Ultra-Wideband (IR-UWB) radar, with its excellent characteristics of high resolution and large bandwidth, has become a research hotspot in the fields of perception and ranging, smart home and so on. However, when radar sampling rates are low, It's hard to recognize human activity only based on radar images. Also when the amplitude of human movement is too tiny, it is difficult to recognize the human activitise just by the energy amplitude of the echo. This paper proposes a novel method to classify small amplitude human chest motions by time-domain signal from Impulse Radio Ultra-Wideband (IR-UWB) radar with a low sampling rate. Based on the energy and phase of the radar echoes, our method can detect the onset of human's motion and differentiate between normal breathing and apnea. Additionally, the amplitude of human's motion can be distinguished based on the distance range spanned by the echoes. Finally, a two-step classification method is employed to distinguish between yawning and coughing in small motions. The paper reports a classification precision of 0.9186, recall rate of 0.9163, and F1 score of 0.9172 for the proposed method under experimental conditions. The experimental performance outperformed the adopted comparison method based on MobileNetV3. We also tested the performance of the proposed method under different angle and distance conditions, which demonstrates the feasibility of our method.

Comparison of analysis methods for separating and recognizing multicomponent radar signals

This paper proposes a model for separating and recognizing a mixture of radar signals using combined multiresolution methods and convolution neural networks. The model involves three main steps: separating the signal into individual components using the Multiresolution analysis (MRA) methods: Empirical mode decomposition (EMD), Variational mode decomposition (VMD), and Maximal overlap discrete wavelet packet transform (MODWPT); transforming these components into the time-frequency domain using Wigner-Ville distribution (WVD) and storing them as images; and then feeding these images into the SqueezeNet for recognition. These multiresolution methods are then compared based on three criteria: The number of successful separations, the SNR ratio of the input signal, and the correlation between the separated signal components and the original signal components. Additionally, we evaluate the performance of the SqueezeNet with real-time signals.

Repeated K-means Clustering Algorithm For Radar Sorting

In modern electronic warfare, a number of radar emitters are in operation, causing radar receivers to receive high-density signal pulses that occur simultaneously. To analyze the radar signals more accurately and identify enemies, the sorting process of high-density radar signals is very important before analysis. Recently, machine learning algorithms, specifically K-means clustering, are the subject of research aimed at improving the accuracy of radar signal sorting. One of the challenges faced by these studies is that the clustering results can vary depending on how the initial points are selected and how many clusters number are set. This paper introduces a repeated K-means clustering algorithm that aims to accurately cluster all data by identifying and addressing false clusters in the radar sorting problem. To verify the performance of the proposed algorithm, experiments are conducted by applying it to simulated signals that are generated by a signal generator.

Investigation of the Crash Process of China Eastern Airlines Flight MU5735 Based on Seismic Data

Abstract Investigating aircraft accidents is a critical and challenging endeavor in the field of aviation safety. Current practices in seismology-based air accident investigations focus on locating crash sites to aid in the search and recovery of missing aircraft. In this study, we conducted an analysis of the China Eastern Airlines Boeing 737–800 crash on 21 March 2022, by leveraging three-component seismic records acquired from a location 21 km away from the crash site, in conjunction with publicly available radar data from Flightradar24 (an online service). Our approach helps to reconstruct the flight state of the aircraft at the time of the impact by modeling the dynamics of the impact with the Earth’s surface. Through an inversion process, we successfully obtained the force–time function and determined the direction of the impact force. In addition, we conducted inquiries into the weight of the 737–800 airliner, which enabled us to estimate the speed of the aircraft at the time of the accident to be about 200–264 m/s using the momentum theorem. These results are in agreement with time of the last radar signal and the measured dive angle of the airplane, providing valuable insights into the circumstances surrounding the crash.

Radar-based elderly fall detection using SPWVD and ResNet Network

Abstract Falls in the elderly population present substantial health risks, often resulting in morbidity and a decline in quality of life. Conventional fall detection methods, including wearable devices and cameras, are hindered by issues such as variable lighting conditions and privacy considerations. Radarbased fall detection has emerged as a promising alternative, providing an unobtrusive method. This study aims to classify fall detection using Smoothed Pseudo Wigner-Ville distribution (SPWVD) images and ResNet-50 model. For this, an online publicly available radar database comprising 15 subjects is utilized. Radar signals is processed into SPWVD timefrequency representation images for analysis. The SPWVD images is fed into the ResNet-50 model. Experiments are performed and performance is evaluated using 10- fold cross validation. The proposed approach is able to distinguish elderly fall. Using ResNet-50 model, the approach yields a maximum average classification f-measure, area under curve score, precision and, sensitivity of 67.15%, 52.31%, 50.55% and, 99.88%, respectively. Hence, the proposed framework holds promise for accurately and efficiently detecting falls among the elderly population within their private environments.

Deinterleaving Method for Radar Signals With Dwell and Switch Pulse Repetition Intervals in a Prior Unknown Scenario

Deinterleaving Method for Radar Signals With Dwell and Switch Pulse Repetition Intervals in a Prior Unknown Scenario

Photonic-assisted angle-of-arrival measurement system for both broadband and single-frequency radar signals

Photonic-assisted angle-of-arrival measurement system for both broadband and single-frequency radar signals

Accurate Low-Angle Tracking Using Successive Interference Cancelation and Single-Tone Frequency Estimation

This paper tackles the low-angle target tracking problem caused by surface-reflected multipaths through successive interference cancelation (SIC) and single-tone frequency (STF) estimation. The coherent specular multipath component in the received radar signal is canceled through SIC, after which the angle-of-arrival (AoA) of the direct path is accurately estimated by applying STF estimation to the interference-suppressed signal. To further improve estimation accuracy, we develop an iterative process where SIC and STF are sequentially repeated. Drawing on the AoA of the direct path estimated using STF, a more accurate AoA of the specular path is obtained by exploiting their geometrical relationship between the two paths, which is then used to suppress the specular multipath in the received radar signal more precisely during the next SIC process. Computer simulations verify that the proposed angle estimation method using SIC and STF (AE-SICSTF) outperforms conventional angle estimation methods. Furthermore, while computational complexity analysis shows that the complexity of each iteration in AE-SICSTF is comparable to that in three-dimensional beam-domain maximum likelihood, it is also found that the former requires less than 10 iterations to converge the root mean square error performance, resulting in considerably less computing time than the refined maximum likelihood and root-MUSIC algorithms.

Elevation Angle Estimation in a Multipath Environment Using MIMO-OFDM Signals

It is challenging to estimate the elevation angle of low-altitude targets due to the multipath effect. Various signal processing techniques have been proposed to mitigate these effects, including the use of multi-frequency signals as opposed to single narrowband signals. However, the optimal type of multi-frequency signals and their effective utilization have not been thoroughly explored. Compressive sensing was also proposed as a high-resolution angle estimation method. But, that was conducted with narrowband signals. In this paper, we employ MIMO-OFDM signals along with a block sparse Bayesian learning fast marginalized (BSBL-FM) method. This combination allows for the effective processing of multi-frequency signals and provides high resolution estimates. The MIMO-OFDM approach represents radar signals in a block-sparse matrix form, and the BSBL-FM method leverages this sparsity to achieve high-resolution angle estimates. Simulation results demonstrate that our method can accurately estimate angles at extremely low altitudes where the elevation angle is less than 1 degree.

Fast distance and velocity estimation in parameter agility radar using CM-CS-OMP and CM-MUSIC methods

Radar applications face the challenges of interference and signal processing demands. Existing parameter-agility anti-jamming methods grapple with high computational complexity. To address this challenge, our paper proposes two methods: Conjugate Mixing Compressed Sensing Orthogonal Matching Pursuit (CM-CS-OMP) and Conjugate Mixing Multiple Signal Classification (CM-MUSIC). These methods significantly reduce signal processing complexity by constructing parameter agility signal pairs for conjugate mixing to eliminate the distance phase term in the post-matched filtering signal. Subsequently, we estimate velocity within the CS and MUSIC frameworks. While CM-CS-OMP excels in scenarios with a small number of targets and performs better at low signal-to-noise ratios, CM-MUSIC is more efficient in multi-target environments due to its lower sensitivity to the number of targets, offering complementary advantages depending on the application context. Through theoretical analysis and simulation experiments, we demonstrate that the CM-CS-OMP and CM-MUSIC methods significantly reduce processing complexity, achieving a time cost reduction of approximately 90% compared to existing CS-OMP techniques, with only a negligible decrease in parameter estimation performance. This trade-off between complexity and performance opens new avenues in radar signal processing for autonomous driving and Internet of Things applications, offering a balance crucial for these technologies’ practical deployment.

Stereooptical Methods of Sea Surface Processes Registration

This work presents the optical methods for measurements of physical parameters on sea surface also covered by ice by using stereo cameras. New method of images with sea ice processing is offered. It can detect areas of open water and sharp edges on ice, which influence on radar signal refraction. This method is based on area of interest detection on optical images, statistical parameters for each area calculation, classification and local level definition to recognize needed structures. By using stereo system perspective distortions can be corrected and physical parameters of wind waves and sea ice can be calculated. This method was probe on stereo images obtained from 90th voyage of R/V «Akademik Mstislav Keldysh» in the Laptev Sea.The results of ripple waves velocity measurements in case on open water by using development previously stereo method is presented. Comparison with Doppler shift of microwave signal is made.The methods presented in this paper are of interest in field experiments simultaneously with the use of coherent radar stations for the quantitative interpretation of radar data, as well as for remote monitoring of the sea surface and ice conditions methods development.

Enhancing Weather Target Detection with Non-Uniform Pulse Repetition Time (NPRT) Waveforms

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of [−50,50] m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar’s detection performance and provide a new method for realizing the multifunctional observation of radar in different applications.

Hierarchically structured composite with 3D interlocking architecture for multi-band defense

The development of high-performance electromagnetic wave absorbing (EMWA) materials for radar stealth applications poses a persistent challenge, particularly when addressing the demanding requirements of diverse frequency bands in increasingly complex and dynamic environments. Precisely tailoring the microstructure of these materials presents a powerful strategy for achieving multifunctional integration and unlocking new possibilities for practical applications. In this work, we present a novel hierarchically structured composite foam with 3D interlocking architecture, fabricated through a straightforward foaming and calcination process. It features a unique dual three-dimensional continuous phase structure that facilitates the formation of an efficient conductive network, promoting free electron mobility and enhancing polarization loss. The composite demonstrates exceptional EMWA performance, exhibiting a wide effective absorption bandwidth (EAB) of 6.1 GHz and a remarkable absorption intensity of -63.2 dB along with RCS reduction value up to 28.7 dB m2. Furthermore, the composite illustrated strong infrared stealth properties and efficient photothermal conversion abilities. When heated to 56°C, its surface temperature only increased by 4°C in 10 minutes. Under direct sunlight, its surface temperature rose from 21°C to 71°C in just 6 minutes. Lightweight, flexible, and adaptable to diverse environmental conditions, the HCC composite holds significant promise as a multi-spectrum defense material. Its versatility makes it an ideal candidate for integration into a wide range of equipment, clothing, and wearable technologies, offering advanced capabilities for radar, infrared, and visible light signature reduction.

Multi-Channel Lightweight Contrast Prediction Coding for Features Extraction of Radar Emitter Signals

This letter presents a novel multi-channel improved contrastive predictive coding (CPC) method to extract signals features. Specifically, to extract low pulse width radar signals features and meet the real-time requirements of signals identification, we design a lightweight encoder to realize the CPC features encoding function. Then, we construct a multi-channel CPC features decoder to mine and extract subtle individual signals features from the perspective of multi-domain and multi-channel information input. Simulation results verify the effectiveness of our proposed method, which can achieve state-of-the-art results in both accuracy and running time compared to the existing optimal methods. All our models and code are available at https://github.com/jn-z/MC-CPC.

Bird-inspired reflexive morphing enables rudderless flight.

Gliding birds lack a vertical tail, yet they fly stably rudderless in turbulence without needing discrete flaps to steer. In contrast, nearly all airplanes need vertical tails to damp Dutch roll oscillations and to control yaw. The few exceptions that lack a vertical tail either leverage differential drag-based yaw actuators or their fixed planforms are carefully tuned for passively stable Dutch roll and proverse yaw. Biologists hypothesize that birds stabilize and control gliding flight without rudders by using their wing and tail reflexes, but no rudderless airplane has a morphing wing or tail that can change shape like a bird. Our rudderless biohybrid robot, PigeonBot II, can damp its Dutch roll instability (caused by lacking a vertical tail) and control flight by morphing its biomimetic wing and tail reflexively like a bird. The bird-inspired adaptive reflexive controller was tuned in a wind tunnel to mitigate turbulent perturbations, which enabled PigeonBot II to fly autonomously in the atmosphere with pigeon-like poses. This work is a mechanistic confirmation of how birds accomplish rudderless flight via reflex functions, and it can inspire rudderless aircraft with reduced radar signature and increased efficacy.

Temporal Associations Between Polarimetric Updraft Proxies and Signatures of Inflow and Hail in Supercells

Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth and cross-sectional area and quantitative characteristics of the radar reflectivity field. This study expands upon prior work by examining temporal associations between supercell polarimetric radar signatures, incorporating a dataset of relatively discrete, right-moving supercells from the continental United States observed by the Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Cross-correlation coefficients were calculated between the ZDR column area and depth and the base-scan hail area, ZDR arc area, and mean ZDR arc value. These correlation values were computed with a positive and negative lag time of up to 45 min. Results of the lag correlation analysis are consistent with prior observations indicative of storm cycling, including temporal associations between ZDR columns and inferred hail signatures/ZDR arcs in both tornadic and nontornadic supercells, but were most pronounced in tornadic storms.

Study of Radio Reconnaissance Receivers Designed on the Basis of Solid-state Filters

Radio intelligence systems are vital technological resources that contribute significantly to contemporary security, defense, and intelligence efforts. They enable the monitoring of adversaries' communication signals, the decryption of encrypted information, and the acquisition of strategic advantages. These systems are essential for military operations, counter-terrorism efforts, national security, and strategic planning. As radio intelligence continues to evolve, it is anticipated that more effective and multifunctional systems will emerge in the future.In modern electronic warfare, radio reconnaissance stations are essential for effective signal detection, classification, and geolocation of enemy communication and radar signals. Current advancements in radio reconnaissance technology focus on enhancing measurement accuracy and reliability, crucial for precise threat identification and situational awareness. Key improvements include implementing high-resolution digital signal processing algorithms, advanced filtering techniques, and real-time data fusion to increase detection precision across complex environments. Furthermore, there is a strategic emphasis on reducing the physical dimensions and weight of these stations to improve mobility and operational efficiency in diverse field conditions. Innovations in miniaturized components, lightweight materials, and compact antenna arrays are central to these design objectives, facilitating deployment in both manned and unmanned platforms. This approach not only optimizes the performance of reconnaissance stations but also broadens their applicability across various operational scenarios, making them integral to modern defense systems. In the article, the application of solid-state filters in the spectrum analyzers of radio reconnaissance receivers was considered in order to increase the measurement accuracy and reliability of radio reconnaissance stations, and to reduce their size and weight. In the article, the modeling of load-connected filter structures was conducted to calculate quasi-statistical potential diagrams, which allows for the optimization of their parameters and reduces the design time of devices based on these filters. The research work was carried out in military unit No. N.

Learning Omni-Dimensional Spatio-Temporal Dependencies for Millimeter-Wave Radar Perception

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficiently further utilize them for perception tasks. This paper rethinks the approach to modeling radar signals and proposes a novel U-shaped multilayer perceptron network (U-MLPNet) that aims to enhance the learning of omni-dimensional spatio-temporal dependencies. Our method involves innovative signal processing techniques, including a 3D CNN for spatio-temporal feature extraction and an encoder–decoder framework with cross-shaped receptive fields specifically designed to capture the sparse and non-uniform characteristics of radar signals. We conducted extensive experiments using a diverse dataset of urban driving scenarios to characterize the sensor’s performance in multi-view semantic segmentation and object detection tasks. Experiments showed that U-MLPNet achieves competitive performance against state-of-the-art (SOTA) methods, improving the mAP by 3.0% and mDice by 2.7% in RD segmentation and AR and AP by 1.77% and 2.03%, respectively, in object detection. These improvements signify an advancement in radar-based perception for autonomous vehicles, potentially enhancing their reliability and safety across diverse driving conditions.

Emotion Recognition in a Closed-Cabin Environment: An Exploratory Study Using Millimeter-Wave Radar and Respiration Signals

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user’s experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.

RIS-aided integrated sensing and communication: Beamforming design and antenna selection

This work considers an integrated sensing and communication system, where a reconfigurable intelligent surface (RIS) is utilized to manage interference and radar signals. The sensors are attached to the RIS to sense multiple targets. A joint design of the base station transmit beamforming and RIS phase shift matrix is proposed to minimize total interference and maximize the worst received signal power at the RIS sensors. Due to highly coupled transmit beamforming and RIS phase matrices, the optimization problem is decoupled into two subproblems and solved iteratively by semidefinite programming and a manifold-based Riemannian steepest descent algorithm. We further design energy-aware beamforming to eliminate the interference induced by radar probing signals. Antenna selection with the ℓ0 norm is introduced to exclude redundant antennas while maintaining the sufficient multiple beams for multiple users and targets with minimized required antennas. Due to the nonconvexity of the ℓ0-norm, we relax the number of active transmit antennas as a weighted ℓ1-norm and employ a concave approximation for the constraint on the radar beampattern. Numerical results illustrate that the proposed algorithms can effectively reduce interference and strengthen the received signal power for radar sensing, achieving mutual benefit for communication and sensing performance.