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.

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.

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT- $\mu $ RadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°). A lightweight DCNN architecture ( $\mu $ RadNet) is also designed and trained with the prepared DIAT- $\mu $ RadHAR dataset comprising 3780 samples. The performance and recognition accuracy of $\mu $ RadNet is statistically computed, and the results are compared to the state-of-the-art (SOTA) CNN models. The $\mu $ RadNet DCNN model outperforms the SOTA CNN models, giving 99.22% of overall classification accuracy, 0.09M parameters, and 0.40G floating point operations (FLOPs) with minimal false negative/positive rates. The time-complexity of the designed lightweight $\mu $ RadNet DCNN model is 0.12 s, which evidences the suitability of our DCNN model for the on-device implementation.

In this study, the authors present the accurate imaging of the behavior of simultaneous operations of multiple low radar cross-section (RCS) aerial targets. Currently, the popularity of low RCS targets is increasing day by day, and detection and identification of these targets have become critical issues. Micro-Doppler signatures are key components for detecting and identifying these low RCS targets. For this, an innovative approach is proposed along with the smooth pseudo-Wigner–Ville distribution (SP-WVD) and adaptive filter bank to improve the attenuation of cross-term interferences to generate more accurate images for the micro-Doppler signatures/patterns of simultaneous multiple targets. A C-band (5.3 GHz) radio-frequency (RF) sensor is designed and used to acquire the micro-Doppler signatures of aerial rotational, flapping, and motional low RCS targets. Digital pipelined-parallel architecture is designed inside the Xilinx field-programable gate array (FPGA) for fast sensor data collection, data preprocessing, and interface to the computer (imaging algorithm). The experimental results of the proposed approach are validated with the results of the classical short-term Fourier transform (STFT), continuous wavelet transform (CWT), and smooth pseudo-Wigner Ville distribution (SP-WVD). Realistic open-field outdoor experiments are conducted covering different simultaneous postures of (i) two-/three-blade propeller/roto systems, (ii) flapping bionic bird, and (iii) kinetic warhead targets. The associated experimental results and findings are reported and analyzed in this paper. The limitations and possible future research studies are also discussed in the conclusion.