Mingjiu Lv

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.

The micro-motion demonstrates a detailed description of the target’s motion, modulating its radar signal in terms of echo, phase and imaging. In this article, we propose a micro-motion feature extraction method based on multi-domain parameter estimation and phase compensation. This method analyzes the periodic peak characteristics of rotor targets in both time and frequency domains. Then, by designing a sliding window peak search algorithm, multiple peaks are extracted, and these are used to estimate the time-domain peak frequency and frequency-domain spectral interval. By comparing the peak frequency in the time domain and the spectral interval in the frequency domain, the odd and even numbers of the rotor target blades are determined, and the rough parameter estimation of the blade number and rotational speed of rotor targets is obtained. Using the estimated parameters to construct compensation factors for phase compensation of the signal, the rotor target blade number is determined through the compensated signal, achieving a more accurate estimation of the blade number and rotational speed and further improving the feature extraction accuracy. The simulation results demonstrate that the method is capable of swiftly and efficiently extracting the features of propeller aircraft and helicopters and has good performance in the case of low SNR or clutter. The effectiveness of the method has also been verified through measurement data.

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.

Aiming at improving the performance of sparse aperture ISAR imaging under the condition of phase error, an efficient structural sparse imaging algorithm with joint phase autofocusing is proposed in this letter. Firstly, an azimuth sparse ISAR imaging model containing phase error is constructed. To fully utilize the structural sparse characteristics of the imaging target, the above imaging model is further transformed into an l ₁ norm optimization problem using structural weighting. Secondly, leveraging fast iterative shrinkage-thresholding algorithm (FISTA), the phase error estimation and the target structure weight updating are integrated into the image reconstruction framework. By solving this compound optimization problem iteratively, the final high-resolution ISAR imaging results are obtained. Finally, the experimental results of the measured data show that the proposed algorithm can achieve well-focused image efficiently under phase error conditions, and has a remarkable imaging performance under low signal-to-noise ratio (SNR) and sparse aperture (SPR) conditions due to the utilization of the sparse structure of the target.

In order to investigate the physical mechanism of the flashes tilting in the micro-motion echoes of the rotor targets under near-field detection, this paper analyzed the physical mechanism of the echo flashes tilting phenomenon of rotor targets based on the scattering point model. It establishes precise definitions for near-field and far-field detection conditions applicable to rotor targets. Firstly, a unified echo model of the rotor targets from near-field and far-field is developed. Secondly, we derive the echo expression from the echo model and analyze the physical mechanism of the flashes in near-field and far-field conditions, respectively. Moreover, the paper explicitly examines the phenomenon of flashes tilting in near-field conditions, shedding light on the factors influencing the duration of this occurrence. Building upon these insights, the study defines the conditions for near-field and far-field detection of rotor targets, which, in turn, determine the timing of flash tilting. Finally, to validate the theoretical framework, a meticulous amalgamation of simulations and experimental data is conducted. These research findings hold substantial promise for enhancing the precision of rotor target modeling. This, in turn, offers invaluable theoretical and practical contributions to the realm of rotor target detection and fault analysis.

A signal time‐frequency results inversion method is proposed for extracting micro‐motion features of rotor targets under low signal‐to‐noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro‐motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time‐frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time‐frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time‐frequency results containing sinusoidal modulation based on the flash's features in the time‐frequency results. Based on the inversion time‐frequency results, the GS‐IRadon algorithm is used to extract micro‐motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time‐frequency results under low SNRs, providing a new technical approach for micro‐motion feature extraction. Time‐frequency results inversion is a novelty method used to achieve micro‐motion feature extraction of 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.

Compressive sensing (CS) based methods have been widely used for sparse inverse synthetic aperture radar (ISAR) imaging. However, many CS-based methods are sensitive to the selection of model parameters, and the residual phase error of the echo also causes trouble for imaging and autofocusing. To address these problems, a novel deep learning approach, named as 2D-IADIANet, is proposed to achieve 2D sparse ISAR imaging with 2D phase error estimation in this paper. First, a 2D ISAR sparse echo model with 2D phase error into account is established, and a 2D-ADMM frame-work-based method, dubbed as 2D-IADIA, is presented to solve this compound reconstruction problem. Second, 2D-IADIA is further unfolded and mapped into a deep network form by integrating with 2D phase error compensation network. Moreover, all adjustable parameters can be learned adaptively by training the network through back propagation algorithm in complex domain directly. At last, experiment results verify that the well-learned 2D-IADIANet, which is only trained by a small amount of simulation samples, can also be generalized to measured data application. Especially, owing to the good performance of the network, the proposal has a superior reconstruction performance than 2D-IADIA under the low 2D sample rate and/or signal-to-noise ratio scenarios.

Sparse inverse synthetic aperture radar (ISAR) imaging can be generally achieved by compressed sensing (CS) methods because sparse sampling disables the approach of conventional range Doppler. However, the CS‐based methods have to convert the matrix into a vector when the random sampling is adopted, which results in huge memory usage and high computational complexity. Note that matrix completion (MC) is suitable for matrix operations, which can recover random sparse data directly based on the low‐rank property of the echo matrix. In order to improve the performance of the conventional MC approaches, a novel reweighted matrix completion method for sparse ISAR imaging is proposed in this paper. To avoid using the same singular value thresholding of the traditional MC method to achieve a low‐rank solution, the reweighted scheme for singular values is applied to restrain the rank. Furthermore, the weights of the current iteration are updated with their singular values obtained in the former iteration, rather than using a fixed value. Then, the alternating direction method of multipliers is utilised to alternatively optimise the proposed model, which has an improved computational efficiency. Once the full data is recovered, the ISAR image can be achieved via the conventional 2D inverse fast Fourier transform. Experimental results based on measured data validate the proposed approach that can result in improving imaging performance within several seconds, which is tens of times faster than some reported low‐rank‐based methods.