Effective Radar Cross Section Research Articles

Tunable spectral properties of PVB/WS2 impregnated fabrics in VIS, IR and RF part of the EMS

The application of nanomodified polymer impregnation on knitted polyester fabrics was studied to improve camouflage characteristics in VIS, IR, and RF domains to enhance multispectral camouflage protection. Nanoparticles of tungsten disulfide (WS2) were dispersed in an ethanol solution of poly (vinyl butyral) (PVB) and applied as a thin impregnation on polyester fabrics already dyed in camouflage shades. A reference sample impregnated only with PVB, without WS2, was also prepared in the same way. Parallel to the impregnated fabrics, a neat knitted polyester textile sample was submitted to all the examination methods. Diffuse reflection, specular gloss, and color coordinates were measured for all the samples. Moreover, the samples were observed using IR thermography in medium and long-wavelength infrared parts of the electromagnetic spectrum. The best suppression (≥20%) in the IR range of EMS was achieved for the dark green shade sample by applying PVB alone and/or adding PVB/ WS2. RF domain measurement analysis of how impregnated fabrics influence effective Radar Cross Section (RCS), K band radar was used, with the measurements done in terrain conditions. The decrease of the mean RCS value between 11% and 41% was achieved. The obtained results indicate that the addition of WS2 has a positive effect on the observed characteristics and that there is a possibility of using this newly impregnated knitwear in the wider area of the EMS as a camouflage material.

Sleep Posture Recognition With a Dual-Frequency Microwave Doppler Radar and Machine Learning Classifiers

An automated, robust, noncontact sleep posture recognition technique is proposed in this letter, which uses optimizable (Bayesian hyperparameter tuning) machine learning (ML) classifiers applied to dual-frequency (2.4 GHz, 5.8 GHz) monostatic continuous-wave radar-measured effective radar cross section and chest displacement. The technique is demonstrated to accurately recognize three different key sleep postures categories for 20 participants, with greater accuracy and computational efficiency than prior published research involving either a custom ML model or threshold-based assessment. Three ML classifiers (K-nearest neighbor, support vector machine (SVM), and decision tree) were assessed, with an SVM using a quadratic kernel achieving an accuracy of 85 and 80%, at 2.4 and 5.8 GHz, respectively, and the decision tree classifier recognizing sleep postures in less than 2 min with 98.4% accuracy for dual-frequency combined measurements.

Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

While Doppler radar can be used to measure cardiopulmonary vital signs during sleep, meaningful diagnostic assessments are often subject to knowledge of a subject's changing sleep posture. The torso Effective Radar Cross Section (ERCS) and displacement magnitude were studied for 20 human subjects in three imitated sleep posture categories using a dual-frequency Doppler radar system in an exploratory examination of the feasibility of using radar to recognize body orientation. Box plot statistical analyses were performed for comparative assessment of ratio variations in ERCS and respiration depth for three different imitated sleep postures. The observed statistical trends and correlations were applied to a physical model to develop posture decision algorithms with initial supine posture data used as a reference. A single-frequency algorithm tracked postures without error for 90% of the subjects using 2.4 GHz data, and 80% using 5.8 GHz data. As accuracy limitations were complementary, a dual-frequency algorithm was developed which recognized postures without error for 100% of the subjects.

Millimeter-Wave Pulse Radar Scattering Measurements on the Human Hand

We investigate the backscattering of low-power millimeter-wave pulses (wavelets) on the human hand in order to determine the detection limit of scattering features. Using an inhouse wavelet radar setup with a nominal spatial resolution of 2.29 cm, we measure a hand in three different postures: a flat hand, a fist, and a hand with raised index finger. For the latter, we are able to resolve backscattering from at least two different scattering centers, attributed to the heel of the hand and the finger. The effective radar cross section in the measurements was in the range from -29.5 to -35.1 dBsm. We demonstrate that detecting scattering features from the hand with an equivalent isotropically radiated power spectral density of -68.5 dBm/MHz is possible. This shows that, compared to most conventional radar systems operating close to the regulatory emission limits (13 dBm/MHz), the energy of the transmitted waveform can be significantly reduced. The result shows that low-power radar systems for gesture recognition are feasible using pulsed systems with ultrashort pulses and low duty cycles. This is key for integration in battery-powered devices.

USING ACCUMULATION AND SIMPLE VOTING METHOD FOR RECOGNIZING CLASSES OF TARGETS BASED ON JOINT DISTRIBUTION OF PAIRS OF ECHO SIGNALS AMPLITUDES

With the development of the defense industry and aviation, there is an increasing number of different air targets. In this regard, the detection and identification of the type of target is currently a actual problem. The algorithm for recognizing object classes based on a two-dimensional probability distribution density of the correlating amplitudes is known from the recently written works. In this paper the methods of increasing the probability of correct radar target classes recognition are considered. The methods are applied with regard to recognition algorithm, which makes a decision based on two attributes (effective radar cross-section and correlation coefficient of the received echo amplitudes samples).The averaging and the simple voting methods were used for increasing the recognition reliability. In this paper is considered evaluation of efficiency of proposed methods. The results of numerical simulation showed that both methods provide the significant gain.

Scattering of a plane electromagnetic wave by a perfectly conducting cylinder in a spherically symmetric radially inhomogeneous absorbing plasma formation

The problem of diffraction of a plane electromagnetic wave by an ideally conducting cylinder of a finite dimensions is solved by the edge wave method. The cylinder is situated in an absorbing radially inhomogeneous plasma formation. The edge wave method is applied for bodies situated in an inhomogeneous medium. Analytic expressions for determination of the dependence of the effective radar cross section of a cylinder that depend on the irradiation angle are obtained, and the corresponding graphical dependences are calculated on the basis of these dependences.

Dual-Frequency Technique for Assessment of Cardiopulmonary Effective RCS and Displacement

In this paper, a technique for the precise assessment of key parameters relating to cardiopulmonary activity is presented. Dual-frequency radar measurements are described with quantitative analyses of the return signal, in terms of intensity and phase modulation magnitude. The first parameter is a measure of the radar cross section of the portion of the torso surface that is moving due to respiration and heartbeat activity. It is defined as the cardiopulmonary effective radar cross section (ERCS). The second parameter corresponds to the maximum displacement of the torso surface in the direction of incidence. This paper presents the first reported dual frequency radar system for determining cardiopulmonary ERCS and displacement. A system calibration procedure using spherical targets is described and displacement measurement is validated for the dual-frequency system. Preliminary testing results consistently showed an ERCS that is larger for the back of the torso and smaller for the side compared to the front, while the respiration depth is smaller in the prone position than in supine.

Automated multi-target tracking with kinematic and non-kinematic information

The authors address an automated multi-target tracking (MTT) problem. In particular, our study is focused on robust data association considering an additional feature and the reliable track management by avoiding track duplications. As the additional feature, the amplitude information is combined with position measurements to improve the performance of the data association so as to effectively distinguish target-originated measurements from clutters. Because of its form of signal-to-noise ratio (SNR), which is often fluctuated according to targets' aspect and effective radar cross section, the usage of the amplitude information is not straightforward. To reduce the certain level of uncertainty of the SNR, the authors propose the SNR estimation algorithm. Moreover, the authors avoid the track duplication problem to achieve the reliability of track maintenance. Specifically, the authors solve the problem by exploiting well-known mean shift algorithm to merge duplications into appropriate clusters. Simulation results demonstrate the effectiveness and high estimation accuracy of the proposed MTT filter compared to existing methods.

Measuring the backscattering from objects based on resistive fiber structures

The backscattering characteristics of objects based on resistive fiber structures are considered. The effective radar cross section of the elements of these structures and the RF absorbing screens based on these structures are measured via the method of synthesized pulses. It is shown that such screens can be used to control the backscattering of actual objects.