A Comparative Analysis of Deterministic Detection and Estimation Techniques for MIMO SFCW Radars

In this paper, the problem of the joint estimation of the range and azimuth of multiple targets in a multiple-input multiple-output stepped-frequency continuous wave radar system is investigated. Three deterministic algorithms solving it through an iterative beam cancellation procedure are described; moreover, an iterative technique, based on the expectation-maximization algorithm, is developed with the aim of refining their estimates. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from a low power wideband radar device. Our results evidence that these algorithms achieve similar accuracies, but at the price of different computational efforts

Next-decade needs for 3-D ionosphere imaging

Accurately imaging the 3-D ionospheric variation and its temporal evolution has always been a challenging task for the space weather community. Recent decades have witnessed tremendous steps forward in implementing ionospheric imaging, with the rapid growth of ionospheric data availability from multiple ground-based and space-borne sources. 3-D ionospheric imaging can yield altitude-resolved electron density and total electron content (TEC) distribution in the target region. It offers an essential tool for better specification and understanding of ionospheric dynamical variations, as well as for space weather applications to support government and industry preparedness and mitigation of extreme space weather impact. To better meet the above goals within the next decade, this perspective paper recommends continuous investment across agencies and joint studies through the community, in support of advancing 3-D ionospheric imaging approach with finer resolution and precision, better error covariance specification and uncertainty quantification, improved ionospheric driver estimation, support space weather nowcast and forecast, and sustained effort to increase global data coverage

Voice Recognition in Noisy Environment Using Array of Microphone

The performance of voice recognition reduces significantly in noisy environments, where the voice signals are distorted severely by addition of noise signal and reverberations. In such environments we can use array of microphone and use beamforming techniques to reduce the effect of noise signals. Presently, microphone-array-based voice recognition is done in two independent stages: first beamforming by array processing and then sending it for recognition. To reduce the effect of noise that is to reduce the distortion in voice waveform array processing algorithm is designed to enhance the signal before feature extraction and recognition. In Beamforming technique an array of sensors, in our case sensors are microphones, is used so that maximum reception can be achieved in a desired specified direction that is in the presence of noise, by the use of estimation of direction algorithm while signals from undesired direction are rejected though they are of same frequency. This is done by using delay and sum method in which the outputs from an array of microphones are delayed by some time so when they are added together, a particular part of the sound field is amplified over other undesired or interfering sources. Then the focussed voice wave is sent to voice recognition algorithm. Correlation algorithm is used for the voice recognition. The algorithm is based on the fact correlation graph between same signal is symmetric and value of correlation is maximum. The system development for this voice recognizer will be done using MATLAB for this project. Using MATLAB a GUI is created which has different function buttons to perform different tasks