An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions

Klaer, P.; Huang, Andi; Sévigny, Pascale; Rajan, Sreeraman; Pant, Shashank; Patnaik, P. C.; Balaji, Bhashyam

doi:10.3390/s20205940

Cited by 30 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can be ideal for small areas and can be controlled from the deck of a small boat [56], but they are limited in terms of flight time and capacity to withstand strong wind conditions. An alternative to multi-rotor drones is single rotor or helicopter drones [57]; they are built for power and durability, with long-lasting flight time with heavy payload capability. However, single rotor drones are harder to fly, and they can be expensive and with more complex requirements.…”

Section: Unmanned Aircraft Systems (Uas)mentioning

confidence: 99%

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

Ubina

Cheng

2022

Drones

View full text Add to dashboard Cite

This paper aims to provide an overview of the capabilities of unmanned systems to monitor and manage aquaculture farms that support precision aquaculture using the Internet of Things. The locations of aquaculture farms are diverse, which is a big challenge on accessibility. For offshore fish cages, there is a difficulty and risk in the continuous monitoring considering the presence of waves, water currents, and other underwater environmental factors. Aquaculture farm management and surveillance operations require collecting data on water quality, water pollutants, water temperature, fish behavior, and current/wave velocity, which requires tremendous labor cost, and effort. Unmanned vehicle technologies provide greater efficiency and accuracy to execute these functions. They are even capable of cage detection and illegal fishing surveillance when equipped with sensors and other technologies. Additionally, to provide a more large-scale scope, this document explores the capacity of unmanned vehicles as a communication gateway to facilitate offshore cages equipped with robust, low-cost sensors capable of underwater and in-air wireless connectivity. The capabilities of existing commercial systems, the Internet of Things, and artificial intelligence combined with drones are also presented to provide a precise aquaculture framework.

show abstract

Section: Unmanned Aircraft Systems (Uas)mentioning

confidence: 99%

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

Ubina

Cheng

2022

Drones

View full text Add to dashboard Cite

show abstract

“…A significant challenge in the use of micro-Doppler and ISAR imaging techniques is a large amount of radar bandwidth required for processing and generating these images [32], [37]. For example, to generate a high-resolution micro-Doppler of a rotating propeller, one needs to generate a pulse repetition frequency that is at least four times the maximum Doppler shift induced by the propeller [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Ezuma¹,

Anjinappa²,

Semkin³

et al. 2021

Preprint

View full text Add to dashboard Cite

This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-tonoise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.

show abstract

“…Also, due to the requirement of the Nyquist-Shannon theory, to resolve the micro-Doppler signatures of a rotating propeller, the pulse repetition frequency (PRF) of a radar needs to be at least four times the maximum Doppler shift induced by the propellers [15]. This requirement could pose a design challenge for surveillance radars, especially those used in airports [16]. Besides, due to the small dimensions of many drones, a larger signal-tonoise ratio (SNR) is required for a radar to capture the micro-Doppler modulations of its rotating propellers [16].…”

Section: Literature Overviewmentioning

confidence: 99%

“…This requirement could pose a design challenge for surveillance radars, especially those used in airports [16]. Besides, due to the small dimensions of many drones, a larger signal-tonoise ratio (SNR) is required for a radar to capture the micro-Doppler modulations of its rotating propellers [16]. Furthermore, since Doppler radars can only measure radial velocity, it is impossible to perform micro-Doppler velocity if the target UAV has no radial velocity relative to the radar.…”

Section: Literature Overviewmentioning

confidence: 99%

Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

Ezuma¹,

Anjinappa²,

Funderburk³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the paper presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.43% or better is achievable.

show abstract

An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions

Cited by 30 publications

References 19 publications

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

Contact Info

Product

Resources

About