Jun Yang

The micro-motion in radar echoes can produce time-frequency flashes, the characteristics of which vary markedly among different targets. However, the physical mechanism governing such flash variations has not yet been fully elucidated. To address this gap, this study investigates the mechanism responsible for variations in the time-frequency flashes of rotor targets. Pronounced changes in the time-frequency flash patterns of rotor echoes are observed as the blade number or rotational speed increases. Based on the established scattering point model, the time-frequency characteristics of the echoes are analyzed using the short-time Fourier transform. On this basis, the underlying mechanism is derived, and the conditions under which the phenomenon occurs are identified. The theoretically derived conditions are validated through numerical simulations. The results indicate that an excessive rotational speed or an increased blade count causes the blades to traverse an angular interval greater than a certain range within a single slow-time interval, thereby suppressing the resolvable temporal variation in the instantaneous frequency and ultimately causing the frequency time-varying characteristics to disappear. Moreover, the STFT window length also governs the visibility and representation of the flash characteristics. Clarifying this mechanism provides a theoretical basis for aircraft target feature extraction and recognition and offers practical guidance for rotor target discrimination in practice.

This paper thoroughly investigates the occurrence of the flash phenomenon in rotor targets. The flash phenomenon is discernible in rotor targets across the time and frequency domains. This phenomenon is characterized by the modulation of signal amplitude through the Singer function, resulting in periodic peaks in the time domain. The appearance of these peaks in the time domain corresponds to the presence of a corresponding frequency band in the time–frequency domain. The influence of interference between scattering points on the amplitude and phase of echo is investigated by employing the echo scattering point model and the echo analytical expression as analytical foundations. A more comprehensive examination of the mechanisms that contribute to the emergence of the flash phenomenon in the time and time–frequency domains is undertaken. Furthermore, its objective is to verify the validity of the analysis performed on the mechanism of the phenomenon. Based on theoretical investigations and simulation results, it can be concluded that the flash phenomenon observed in rotor targets is due to interference between particular scattering points. Fundamentally, it can be comprehended as a phenomenon of interference. This research achievement has specific theoretical and practical value in detecting and recognizing rotor targets.

Compressive sensing technique has been widely applied to achieve range-Doppler reconstruction of high frequency radar by utilizing sparse random stepped-frequency (SRSF) signal, which can suppress the complex electromagnetic interference and greatly reduce the coherent processing interval. An important way to improve the performance of sparse signal reconstruction is to optimize the sensing matrix (SM). However, the existing research on the SM optimization needs to design a measurement matrix with superior performance, which needs a large amount of computation and does not consider the influence of the waveform parameters design. In order to improve the superior reconstruction performance, a novel SM optimization approach for SRSF signal is proposed by using two-dimensional ambiguity function (TDAF) in this paper. Firstly, based on the two-dimensional sparse reconstruction model of the SRSFs, the internal relationship between the waveform parameters and the SM was derived. Secondly, the SM optimization problem was directly transformed into the waveform design of SRSFs. Furthermore, on the basis of analyzing the relationship between the mutual coherence matrix of SM and the TDAF matrix of SRSFs, the purpose of optimizing the SM can be achieved by designing the TDAF of the SRSFs. Based on this analysis, a sparse waveform optimization method with joint constraints of maximum and mean sidelobes of the TDAF by using the genetic algorithm was derived. Compared with the traditional SM optimization method, our method not only avoids generating a new measurement matrix, but also further reduces the complexity of the waveform optimization. Simulation experiments verified the effectiveness of the proposed method.

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar.

Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.