Velocity measurement based on inertial measuring unit

Vehicles technology have been a priority area of research over the last few decades. With the increasing the use of electronic components in the automotive industry to measure conditions around the vehicle, the focus of automotive technology development is now leading to the development of active technology. Information on the speed of conventional vehicles is generally still obtained based on the rotation of the wheel, but there are weakness in the system that is the diference between wheel and road through vehicle also changes wheel radius of the vehicle due to wind tube air preasure that can change at any time. In this research used Inertial Measuring Unit (IMU) 6 axis (accelerometer and gyroscope) which have been done filtering by using Kalman filter in order to make output sensor value more stable, results obtained at the test of 0 m/s had an RMS error of 0.8696 m/s when elevation is +450; 0.0393 m/s when elevation is 00; and 0.3030 m/s when elevation is -450. this research is expected to be an exploration for the development of a decent system that is suitable to be used as vehicle speed estimator which is as reliable as it is by using an existing speedometer on a ground vehicle generally regardless of slippage and changes in wind capacity on wheels

A Robust Super-resolution Algorithm in a Low SNR Environment for Vital Sign Radar

We propose a robust super-resolution algorithm for vital sign radar in a low signal to noise ratio (SNR) environment. Conventional approaches, such as fast Fourier transform and super-resolution based algorithms, suffered to provide reliable results due to the limited data length and high noise level. To overcome these limitations, our proposed algorithm utilizes a low-complexity least mean square (LMS) filter and relaxation (RELAX) techniques to achieve robust performance in low SNR environments. To evaluate the effectiveness of our algorithm, we conducted both simulation and experimental studies. Our results show that the proposed method significantly outperforms conventional methods, with Monte-Carlo simulations of respiration and heartbeat achieving an RMSE approximately 7 and 120 times lower than that of the conventional method, respectively. Overall, our algorithm provides a promising solution for robust vital sign detection in challenging low SNR environments