ISAR imaging for maneuvering targets (MT) in sparse aperture (SA) condition is a challenging problem. Range instantaneous Doppler (RID) is useful for ISAR imaging of MT through time-frequency analysis. However, the performance of RID deteriorates in SA, and frequency resolution is limited by the assumption of stationary signal in the time window. To tackle these issues, a complex value parametric efficient sparse Bayesian learning (CPESBL) ISAR imaging algorithm is proposed in this letter. In our algorithm, the one frame signal of MT ISAR imaging is modeled as the multicomponent Chirp signal. This model is solved by CPESBL which contains the complex value efficient SBL (CESBL) with low computational complexity and the Quasi-Newton method estimating the Chirp rate parameter. Then the focused ISAR image can be obtained efficiently. Moreover, a dimension shrinkage strategy is also proposed to further improve the computational efficiency considering the continuity of sequential ISAR images. With a low computational complexity, the proposed algorithm achieved the best image quality index both in simulated and measured data experiments.

In micromotion signature analysis, the radar return signal with missing sampling may cause defocused time–frequency representation (TFR) and thus prevent micromotion characteristics acquisition. To address the issue, we present an adaptive time–frequency distribution reconstruction method based on$L_{1}$regularization. First, the$L_{1}$regularization is expressed as a combination of two$L_{2}$regularizations based on Hadamard product parametrization. Then, the iterated Tikhonov regularization is applied to solve each$L_{2}$regularization alternatively. Moreover, the regularization parameter is updated adaptively based on the matching pursuit principle at each iteration. Finally, the reconstructed TFR is updated based on least-square-error criterion to eliminate the attenuation of signal amplitude. Simulation and measurement data examples have demonstrated the effectiveness of the method.

When echoes of micromotion targets are overlapping in the time-frequency (TF) domain and sampling data are missing, the decomposition of the multicomponent micro-Doppler (m-D) signals is challenging. To address this issue, this letter proposes a method for multicomponent m-D signal decomposition by iterations of the instantaneous frequencies (IFs), individual components, and complex envelopes. To initialize the IFs, the well-focused time-frequency representation (TFR) is obtained by sparse reconstruction of the incomplete data, and then the IFs of the TFR can be estimated by the short-time variational mode decomposition (STVMD) algorithm. After initialization, the IFs, individual components, and complex envelopes are updated by the intrinsic chirp component decomposition (ICCD), alternating direction method (ADMM) of multipliers, and least-square-error criterion (LSEC), respectively. Finally, the proposed method is verified by simulation and application to real data.

Multi-component nonlinear chirp signals (NCSs) widely exist in microwave remote sensing. In some applications, it is necessary to separate NCSs containing close components in the time-frequency (TF) domain. However, the phase-corrupted data may cause a defocused TF signature and prevent individual component extraction. To solve the problem, the optimization model is developed to reconstruct and decompose multi-component NCSs with phase errors and solved by an alternating iterative algorithm. In each iteration, the individual components, phase errors, and the regularization parameter are updated by the alternating direction method of multipliers, least-square-error criterion, and the matching pursuit principle, respectively. Finally, the effectiveness of the proposed method is verified by simulation and real data examples.

Passive radar based on communication signal has a great significance and potential in target detection and recognition for low-altitude surveillance, national air defences, etc. Micro-Doppler (m-D) effect is a unique signature of low-altitude targets with micro-motion, which provides a new technique for target recognition in low-altitude airspace. In this paper, based on orthogonal frequency division multiplexing (OFDM) communication signal, the echoes modelling and analysis of rotating target are operated, and the mathematical expression of m-D is deduced. Furthermore, the rotation features are extracted by a method based on single measurement vector orthogonal matching pursuit (SMV-OMP) algorithm. The feasibility of the proposed method is proved by the simulation results. The method could offer a reference to feature extraction of low-altitude micro-motion targets under passive radar.

In this paper, we proposed a method based on the minimum entropy principle to compensate the motion error. Since the impact of air disturbance and the flight attitude, there is a motion error of the platform motion. The motion error will cause the imaging defocusing, which will influence the quality of the imaging. Firstly, we established the imaging model of down-looking sparse linear array 3-D SAR imaging with motion error. Then, the impact of motion error is analyzed. A method based on minimum entropy principle is proposed to compensate the motion error. Finally, the cross-track signal reconstruction is finished by the compressed sensing. Experimental results demonstrated the effectiveness of the proposed method.

In order to compensate the translational motion and extract micro-Doppler (m-D) feature of space spinning targets, a novel method based on delayed–conjugated multiplication and m-D compensation is put forward. The translational acceleration is estimated by the delayed–conjugated multiplication processing. A set of m-D basis signals are generated by discretizing the m-D parameter domain. Thus after acceleration compensation processing, the echo is compensated by the m-D basis signals. Furthermore, the m-D feature and residual velocity can be achieved by searching the maximum spectral peak. The high-precision m-D feature extraction with lower computation complexity is achieved by using the proposed method. Finally, the effectiveness of the proposed method is validated by simulations.

Aimed at the problem of how to diminish SAR raw data apparently and realize SAR imaging effectively, a new approach for processing SAR raw data combined with Linde-Buzo-Gray (LBG) algorithm and Compressed Sensing (CS) is proposed in this paper. For SAR returned signals, CS is engaged to reduce the sampling number in the pulse duration, and LBG algorithm as a classical vector quantization (VQ) method, is employed to diminish encode number of every sample value. Next, data reconstruction process still contains the two ordinal steps according to LBG algorithm and CS theory, respectively. On the basis of that, the traditional SAR imaging method, Frequency Scaling (FS) algorithm, is carried out to achieve the final SAR image. Simulation results show that the high quality SAR image can be achieved on condition of the SAR raw data is diminished furthermore obviously, which is compared with the traditional method.

The micromotion of a target will generate a micro-Doppler (m-D) effect in the frequency domain. The m-D effect is regarded as a unique property of the target, which has special significance in target detection, identification, and classification. The classical range-Doppler algorithm cannot obtain a clear inverse synthetic aperture radar (ISAR) image due to the m-D effect induced by micromotion dynamics. The m-D effect induced by periodical micromotion is represented as a sinusoidal modulation in a spectrogram, whereas the Doppler induced by a main body is depicted as the form of a straight line. Therefore, the extraction of an m-D signature is transformed into the separation of a sinusoid and a straight line. The cancellation technique is a classical method for removing ground clutter. Based on the same principle, the cancellation technique is applied to the spectrogram in this letter, which successfully achieves the separation of the m-D signature and gets the clearer ISAR image of the main body. The effectiveness and robustness of the algorithm are proved by simulation results.

The micro-Doppler (m-D) effect induced by the rotating parts or vibrations of the target provides a new approach for target recognition. To obtain high range resolution for the extraction of the fine m-D signatures of an inverse synthetic aperture radar target, the stepped-frequency chirp signal (SFCS) is used to synthesize the ultrabroad bandwidth and reduce the requirement of sample rates. In this paper, the m-D effect in SFCS is analyzed. The analytical expressions of the m-D signatures, which are extracted by an improved Hough transform method associated with time-frequency analysis, are deduced on the range-slow-time plane. The implementation of the algorithm is presented, particularly in those extreme cases of rotating (vibrating) frequencies and radii. The simulations validate the theoretical formulation and robustness of the proposed m-D extraction method.