Design and Manufacture of Traveling-wave Electro-optic Modulator for X-band LFM Signal Generation

In this paper, a photonic-based microwave system technology is described, and a traveling-wave electro-optic modulator is designed and manufactured as a key component. The fabricated modulator is composed of a metal diffusion waveguide for optical transmission and a planar waveguide electrode on lithium niobate substrate for microwave transmission. The electro-optic response bandwidth of I and Q channels in a fabricated dual parallel Mach-Zehnder modulator were measured for 27.67 and 28.11 GHz, respectively. Photonic four times up-converted X-band frequency and linear frequency modulated signal were confirmed using the fabricated electro-optic modulator by S-band input signal. The confirmed broadband signal can be applied to a microwave system for surveillance and high-resolution ISAR imaging.

A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets

Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics.

Modelling of the anti-tank guided missile scattering field and radar performance evaluation in terms of missile detection

The study presents the results achieved in an analysis of the variation of the level of an echo signal generated by the FGM-148 Javelin anti-tank missile, received by the active protection system radar with consideration given to the target attack flight profile. For our study, we use the results of modelling of the missile’s scattering field at frequencies of 1, 3, 6, and 10 GHz using the Altair FEKO CAD suite. According to estimated data, a radar using the frequency-modulated signal and operating at a medium radiation power of up to 1 W is able to detect a missile at a distance of at least 1,000 m. The results show that a combination of low power and a continuous broadband signal (100…150 MHz) along with coherent integration to units of seconds ensures concealed operations of the active protection system radar (estimated range of its radiation detection by a reconnaissance radar is not greater than 10 km).

DIGITAL ANTENNA ARRAYS SIMULATION

The article describes a simulation model of a digital antenna array, which can be used in broadband signal transmission systems under the influence of broadband interference. The initial information signal has a speed of 19200 bit / s for the I and Q stream. It is proved that the signal level after applying the diagramming of a digital antenna array is four times higher than the signal level without its use, which confirms the advantage of digital radio systems.

Infrasound measurement system for real-time in-situ tornado measurements

Previous work suggests that acoustic waves at frequencies below human hearing (infrasound) are produced during tornadogenesis and continue through the life of a tornado, which have potential to locate and profile tornadic events and provide a range improvement relative to current radar capabilities, which are the current primary measurement tool. Confirming and identifying the fluid mechanism responsible for infrasonic production has been impeded by limited availability and quality (propagation-related uncertainty) of tornadic infrasound data. This paper describes an effort to increase the number of measurements and reduce the uncertainty in subsequent analysis by equipping storm chasers and first responders in regular proximity to tornadoes with mobile infrasound measurement capabilities. The study focus is the design, calibration, deployment, and analysis of data collected by a Ground-based Local INfrasound Data Acquisition (GLINDA) system that collects and relays data from an infrasound microphone, GPS receiver, and an IMU. GLINDA has been deployed with storm chasers beginning in May 2020 and has provided continuing real-time automated monitoring of spectrum and peak detection. In analysis of sampled severe weather phenomena, the signal measured from an EFU tornado (Lakin, KS) show an elevated broadband signal between 10 and 15 Hz. A significant hail event produced no significant increase infrasound signal despite rotation in the storm. The consistency of these observations with existing fixed array measurements and real-time tools to reduce measurement uncertainty demonstrates the value of acquiring tornado infrasound observations from mobile on-location systems and introduces a capability for real-time processing and display of mobile infrasonic measurements.