Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) become more popular for various defence as well as civil applications. They can also give threat to national security if intentionally operated by any hostile-actor(s). Since, all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture. In this work, a lightweight novel DCNN model (named “DIAT-RadSATNet”) is designed for the accurate SUAV targets: RC plane, 3-short-blade rotor, 3-long-blade rotor, quadcopter, bionic bird, and mini-helicopter + bionic bird; detection/classification based on their m-D signatures. A diversified, X-band (10GHz) continuous wave (CW) radar based, open-field-collected m-D signatures dataset (named as “DIAT-μSAT”) is used for the design/testing of “DIAT-RadSATNet”. A set-of new design principles is proposed through which multi-factors: layers, #parameters, floating point operations (FLOPs), no. of blocks, filter dimension, memory size, no. of parallel paths, and accuracy; optimization is applied via a series of in-depth ablation studies. The novel “DIAT-RadSATNet” module consists of 0.45M trainable parameters, 40 layers, 2.21Mb memory size, 0.59G FLOPs, and 0.21s computation-time complexity. The detection/classification accuracy of “DIAT-RadSATNet”, based on the open-field unknown dataset experiments, falls within 97.1% and 97.3%. A comparative analysis with state-of-the-art (SOTA) DCNN models evidences the betterness, more efficiency, and more accuracy of our novel “DIAT-RadSATNet” architecture.

Protective measures against small unmanned aerial vehicles (UAVs) are vital from a national security perspective. As a result, the importance of surveillance systems that automatically identify and classify low radar cross section (RCS) aerial targets increases. In this work, an indigenously developed continuous wave (CW) (X-band: 10 GHz) radar is used to build a diversified “DIAT- $\mu $ SAT” dataset comprising 4849 micro-Doppler signature images of five different small aerial targets. We also proposed a transfer learning-based deep convolutional neural network (DCNN) approach for classifying low RCS aerial targets. We demonstrated the classification accuracy of 95% and 97%, with VGG16 and VGG19 as feature extractors, respectively, with minimal false-negative and -positive results. The open-field experimental classes covered in this work are: 1) a two-blade rotor; 2) a three-short-blade rotor; 3) a three-long-blade rotor; 4) a quadcopter; 5) a bionic bird; and 6) a two-blade-rotor and bionic bird. We also observed a good classification accuracy (>97%) when more than one target is operated simultaneously.

This survey article discusses the revolutions of wireless communication technologies right from the prehistoric man's fire signals to today's space optical communications. A brief description of evaluations of communication technologies from the ancient Greece to today's mature telecommunication fields is discussed in this review paper. Principle, significance, demonstration and development of free space optical (FSO) communication technology over different decades are discussed. Major FSO channel limitations, temporal and spatial challenges of FSO communication system and their state‐of‐art mitigation techniques are presented. Classical quantitative analysis of reliability of FSO communication, developments on radio over FSO (RoFSO) and hybrid FSO/RF systems are also explained. Advanced developments of FSO communication techniques such as optical free space wavelength division multiplexing (WDM), sub‐carrier multiplexing (SCM), worldwide interoperability for microwave access (WiMAX), visible light communications (VLC) and vehicular visible light communications (VVLC) are reported. Deep space optical communication systems and next generation FSO wireless terrestrial/global network architecture are summarised. Research challenges of FSO systems for internet of things/everything (IoT/IoE), 5G communication, mobile‐network, terahertz spectrum, quantum communication and underwater optical applications are presented. Based on the review, we outlined the challenges that need to be addressed in near‐future researches to realize full potential FSO communication systems.

Recognizing suspicious human activities is one of the critical requirements for national security considerations. Nowadays, designing the deep convolution neural network (DCNN) models suitable for micro-Doppler (m-D) signature-based human activity classification is rapidly growing. However, high computation cost and a huge number of parameters limit their direct/effective usability in field applications. This article introduces an m-D signatures’ dataset “DIAT- $\mu $ RadHAR” covering army crawling, boxing, jumping while holding a gun, army jogging, army marching, and stone-pelting/grenade-throwing, generated using an $X$ -band continuous wave (CW) radar. This article also introduces a lightweight DCNN model, “DIAT-RadHARNet,” designed for those human suspicious activity classification. To reduce the computation cost and to improve the generalization ability, DIAT-RadHARNet is designed with four design principles: depthwise separable convolutions, channel weighting (CHW) based on the importance, different size filters in the depthwise part, and operating different size kernels on the same input tensor. The network has 213 793 parameters with a total of 55 layers. Our extensive experimental analysis demonstrates that the DIAT-RadHARNet model efficiently classifies the activities with 99.22% accuracy, giving minimal false positive and false negative outcomes. The time complexity of the proposed DCNN model observed during the testing phase is 0.35 s. The same accuracy and time complexity are obtained even at adverse weather conditions, low-lighting environments, and long-range operations.

This review paper discusses the complete evolution of free-space optical (FSO) communication, also known as unguided optical communication (UOC) technologies, all the way back to ancient man’s fire to today’s machine-learning-supported UOC systems. The principles, significance, and developments that have happened over the past several decades, as well as installation methodologies, technological limitations, and today’s challenges of UOCs are presented. All the subsets of UOC: FSO communication, underwater optical wireless communication (UOWC), and visible light communication (VLC), with their technology/system developments, potential applications, and limitations are reviewed. The state-of-the-art developments/achievements in (i) FSO channel effects and their mitigation techniques; (ii) radio-over-FSO techniques; (iii) wavelength division multiplexing and sub-carrier multiplexing techniques; (iv) FSO for worldwide interoperability for microwave access applications; (v) space optical satellite communication (SOSC); (vi) UWOC; (vii) photoacoustic communication (PAC); (viii) light-fidelity; (ix) VLC; (x) vehicular VLC (V²LC); and (xi) optical camera communication are reviewed. In addition, the current developments on emerging technologies such as artificial intelligence (to improve the performance of UOC systems), energy harvesting (for the effective utilization of UOC channels), and near-future communication network scenarios (mandatory for secured broadband digital links) are covered. Finally, in brief, to achieve the full potential of UOC systems, challenges that require immediate research attention are summarized.

The convergence of wireless optical communication (WOC) and radio-frequency (RF) systems is a promising technology that overcomes the shortcomings of standalone communication systems. By incorporating reconfigurable intelligent surfaces (RISs) on top of these WOC and RF communication systems, it is possible to circumvent the connection challenges associated with standard line of sight (LOS) communication links. Wireless communication systems with RIS assistance are a promising and evolving technology that enables more efficient and reliable link performance over long distances. The performance of the triple-hop RIS-assisted RF-FSO convergent with the underwater wireless optical communication (UWOC) system is investigated in this article. We considered the fading channel Nakagami-m over the RIS-RF connection and the fading channel Gamma-Gamma (GG) over the RIS-FSO and UWOC links. Then, the average bit error rate (ABER) and outage probability are determined using closed-form expressions. The ABER and outage probability performances of the triple-hop communication system is analysed by varying parameters such as turbulence, misalignment fading, and the number of RIS elements. The obtained results demonstrate an improvement in performance for low turbulence, low pointing error, and an increasing number of RIS elements. Additionally, the data demonstrate the accuracy of the analytical results.