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This article presents a robust technique for inverse synthetic aperture radar (ISAR) image enhancement using bivariate multitaper spectrum estimation with simultaneous concentration. The proposed method aims to average multiple focused ISAR images obtained using orthonormal windows. As a result, an image with an improved concentration and reduced noise is obtained. Compared to state-of-the-art methods, the presented approach efficiently suppresses stationary and non-stationary noise in ISAR images, leading to contrast and entropy enhancement. Extensive simulations are shown to illustrate the superiority of the proposed technique quantitatively, confirming its utility in practical systems. Ultimately, the real-life high-resolution ISAR image processing result is shown, demonstrating the method’s performance in active frequency-modulated continuous wave (FMCW) radars.

This article presents an approach for joint radar sensing and communications based on the orthogonal time–frequency space (OTFS) modulation, applied to a moving target detection during regular digital wireless transmission (TX). Most communication systems estimate and correct channel influence using specially designed reference signals or pilots. Pilots are usually known to the receiver before the TX, and the receiver estimates the channel impulse response (CIR) by analyzing the input (pilots) and output of the channel. Because of the Doppler effect, the movement of reflection sources (or targets in radar terminology) in multipath channels causes oscillations of CIR coefficients. Based on the frequency analysis of time-varying CIR, one can extract the distances and velocities of moving targets when the data is transmitted through the wireless channel. This article describes the application of OTFS with random pilots using both simulations and real-life field tests. It is shown that applied time-varying CIR estimation based on random pilot signals overcomes the OTFS with zero padding during vehicle velocity estimation in very noisy conditions. The proposed OTFS-based sensing method with the CIR-based channel estimation and vehicle detection is experimentally compared with the cross-ambiguity function (CAF) approach commonly used in radars. A thorough analysis shows that the proposed CIR-based approach outperforms the classical CAF-based radar processing in terms of target detection. Finally, the proposed integrated sensing and communication (ISAC) concept, including bit TX, is experimentally verified.