Clutter analysis and simulation in forward scatter micro radar network

Over the past few years, numbers of research have been carried out to investigate the clutter characteristic especially for conventional monostatic and bistatic radar detection (mostly in maritime and airborne using Ultra Wide Band radar and Synthetic Aperture Radar) and not specifically on Forward Scatter Radar (FSR). FSR provides an efficient approach for detection of stealth target, the simplicity of the hardware design, increase the power budget and work in all weather operation. One of the limitations in forward scatter radar is the range resolution. Clutter mainly from the surrounding vegetation is picked up from a large area illuminated by transmitter and receiver which is located on the ground. Vegetation clutter is a significant factor that limits the performance of ground based Forward Scatter Radar. In this research, the analysis is focused on clutter on ground-based Forward Scatter Micro Radar system network where the clutter characteristics are studied for different environmental conditions such as different land sites, wind and weather conditions for different carrier frequencies. These comprehensive analyses are used eventually, to determine the clutter characteristics and are used for clutter modelling in order to create similar clutter-like signal that can be used to develop a synthetic environment for Forward Scatter Radar detection performance analysis in the future. Three main works have been done; 1) real measurements to determine the Clutter characteristic for FSR based on statistical analysis of a number of experiments; 2) modeling and simulation of clutter signal based on real signal characteristics and 3) the comparison of simulated and measured signals

Hand Gesture Recognition Based on Continuous Wave (CW) Radar Using Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) Methods

Human-computer interaction (HCI) is a field of study studying how people and computers interact. One of the most critical branches of HCI is hand gesture recognition, with most research concentrating on a single direction. A slight change in the angle of hand gestures might cause the motion to be misclassified, thereby degrading the performance of hand gesture detection. Therefore, to improve the accuracy of hand gesture detection, this paper focuses on analyzing hand gestures based on the reflected signals from two directions, which are front and side views. The radar system employed in this paper is equipped with two sets of 24 GHz continuous wave (CW) monostatic radar sensors with a sampling rate of 44.1 kHz. Four different hand gestures, namely close hand, open hand, OK sign, and pointing down, are collected using SignalViewer software. The data is stored as a waveform audio file format (WAV) where one data consists of 20 segments, and the data is then examined by using MATLAB software to be segmented. To evaluate the effectiveness of the classification system, principal component analysis (PCA) and k-nearest neighbor (KNN) are integrated. The PCA findings are depicted in Pareto and 2-D scatter plot for both radar directions. The Leave-One-Out (LOO) method is then used in this analysis to verify the accuracy of the classification method, which is represented in the confusion matrix. At the end of the analysis, the classification results indicated that both angles achieved near-perfect accuracy for most hand gestures

Human movement detection and classification capabilities using passive Wi-Fi based radar

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and human-computer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%