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Recent development in drone technology along with its diverse applications has come up with the need to focus on privacy threats due to drone surveillance. Drone detection and identification are state-of-art research topics which are addressed by the RADAR signal processing algorithms. The natural resonance information extracted from the object’s backscattered signal is said to have unique resonance features according to the singularity expansion method (SEM). This article investigates the nature of resonance information using the frequency domain vector fitting (VF) method. The detailed simulation analysis is conducted by considering incident aspect angle variations and a different set of composite canonical objects. The main challenge of drone discrimination is addressed in this article by using the predictor correlator waveform. The minimum value of the quantitative measure of predictor correlator number (PDN) provides the accurate discrimination of the drone. The robustness of this approach is tested by using the noisy object’s signal for the wide range of the signal-to-noise ratios (SNR). The main contribution of this article is the detailed quantitative analysis of the real-time drone models by collecting its scattered fields. Moreover, the mathematical development to get the frequency domain response is discussed by using the measurement of scattered field data. This simulated and measurement investigation was observed to give accurate discrimination using the PDN discrimination approach based on a unique set of resonance poles and residues.

The microstructure of the composite material is a crucial factor in designing a radar absorbing material (RAM). To design and utilize these composite materials effectively as RAMs, accurately predicting their electromagnetic properties is essential. This is typically accomplished through the use of mixing models, which can forecast the behavior of composite materials. In this article, we employ the Maxwell-Garnett (MG) model, a popular mixing model, to model carbon black (CB) reinforced composites. We address the challenges associated with modeling CB composites using the MG model. We investigate two different manufacturing processes, namely: 1) mechanical stirring and 2) homogenizing. We use the best fit between measured permittivity and computed permittivity from the MG model to determine the electrical conductivity and aspect ratio of CB embedded in epoxy resin and verify our model through experimentation. Our findings indicate that both manufacturing processes can be simulated using the MG model and a constant value of conductivity and aspect ratio characterizes a given manufacturing process in the 8–12.5-GHz frequency range. To further test the effectiveness of our model in RAM design, we apply it to predict reflection loss in the 8-12.5-GHz range and experimentally verify the results. Finally, we utilize the mixing model in an analytical approach to develop a multilayer broadband RAM.

An electromagnetic signal scattered from a radar target consists of resonance information, which can be utilized to achieve automatic target discrimination. To extract this resonant information, the short-time matrix pencil method (ST-MPM) is one of the robust approaches. The library of unique extinction pulses (E-pulses) is generated using the target’s resonant poles. Then, the quantitative discrimination measures of E-pulse discrimination number (EDN) and target discrimination number (TDN) are calculated. Recently, the radar targets are evolved with complex structures along with dielectric-coated materials, thus creating difficulty in target recognition. This communication focuses on the discrimination of dielectric-coated composite objects using extracted resonant poles and automatic discrimination measures. The demonstration of a discrimination approach is done based on simulation and measurement tests using complex scale models of aircraft. The robustness of the TDN measure is also investigated by analyzing noise-contaminated object returns for a range of signal-to-noise ratios (SNRs). This analysis shows that the TDN provides superior discrimination for all considered target sets.