In compressed sensing (CS)-based inverse synthetic aperture radar (ISAR) imaging for aircraft, the image of the target can be reconstructed using fewer pulses with random pulse repetition intervals than with the conventional range-Doppler (RD) method. However, the micro-Doppler (mD) effect induced by the non-stationary parts of aircraft still causes defocusing as in RD imaging. A method is proposed to reduce the mD effect in CS ISAR imaging. The CS-based short-time Fourier transform is deployed to reconstruct the time-frequency (TF) spectrogram of echoes. An L-statistics-based algorithm is applied to separate the non-stationary scatterers from a rigid main body in the TF domain. Furthermore, the CS algorithm is used to reconstruct the cross-range image of the main body after mD separation. Compared with direct CS imaging without mD removal, a better image can be obtained. The results of both simulated and real data processing demonstrate the validity of the proposed method.Introduction: In inverse synthetic aperture radar (ISAR) imaging for aircraft, the images are usually contaminated because of the micro-Doppler (mD) effect, which is induced by the non-stationary mechanical structures such as the rotating blades in helicopters and the propellers or turbofans in airplanes. Some mD separating methods based on time-frequency (TF) analysis have been proposed and proved to be effective in conventional range-Doppler (RD) imaging [1,2].Different from RD imaging, compressed sensing (CS) ISAR imaging can reconstruct the image of the target using much fewer pulses with random pulse repetition intervals (PRIs) [3]. However, how to remove the mD effect in CS ISAR imaging is still an open problem. Since the existing mD extracting methods require that the echoes of targets are uniformly sampled in the cross-range dimension, the validity of these methods is challenged in random sampling. To solve this problem, in this Letter we propose a method to reduce the mD effect for CS ISAR imaging.
System distortion is inevitable in wideband imaging radar, which needs to be compensated precisely. In traditional wideband imaging radar for space surveillance, a linear frequency modulation signal is transmitted and the echoed signal is received with dechirping processing. In this case, the system distortion changes with the target range, making it hard to compensate the distortion. However, the direct intermediate frequency sampling (DIFS) signal maintains the complete system distortion and avoids the range variant distortion in the dechirped signal. Therefore, it is more convenient to perform system compensation in DIFS signal. In this paper, the distortion factors affecting the wideband radar systems are introduced. Then, the influence of the amplitude phase distortion on the focusing quality is analyzed in detail. Finally, a system compensation method in the frequency domain based on least squares estimation is proposed. In the proposed method, the compensation vectors are extracted from the calibration tower echoes for DIFS compensation. Inverse synthetic aperture radar imagery of targets can be achieved with improved focus quality. Simulations and real-data experiments confirm the effectiveness of the proposal.
This paper proposes a novel design of intermediate frequency (IF) digital receiver for wideband inverse synthetic aperture radar (ISAR) based on compressed sensing (CS). For the convenience in engineering application, we use random sampling in the digital receiver and make it possible to digitize the wideband IF signal using a commercial off-the-shelf analog-to-digital converter with sub-Nyquist sample rate. Besides, a novel basis for the sparse representation of real-valued ISAR radar echoes is built in this paper, and an orthogonal CS reconstructing algorithm is proposed based on this. Using our proposed method, the complex-valued range profile of target can be directly reconstructed from the subsampled real raw echo. The phase information of target range profile, which is very important for the coherent processing in ISAR imaging, is well reserved during the reconstruction. As a result, the down converter and matched filter, which are essential in conventional radar receiver, can be eliminated in our CS digital receiver. A series of simulation validates our design and demonstrates the feasibility of the sub-Nyquist sampling. The simulation results of ISAR imaging verify the validity and superiority of the proposed orthogonal reconstructing method.
Abstract-Due to the inaccuracies in radar's measurement, autofocus including range alignment and phase compensation is always essential in inverse synthetic aperture radar (ISAR) imagery. Compressed sensing (CS) based ISAR imagery suggests that the image of target can be reconstructed from much fewer random pulses. Because the number of pulses is inadequate and the pulse intervals are nonuniform, conventional phase compensating algorithms can't work in CS imaging. In this paper, an iterative algorithm is proposed to compensate the phase errors and reconstruct high-resolution focused image from limited pulses. In each iteration, the image of target is reconstructed by CS method, and then the estimation of phase errors is updated based on the reconstructed image. By cycling these steps, well-focused image can be obtained. The smoothed 0 algorithm is used to reconstruct the image, and the idea of minimum entropy optimization is used to estimate the phase errors. Besides, a method of extracting range bins in range profile based on amplitude information is proposed, which can reduce the computational complexity and improve the speed of convergence considerably. Both simulation and experiment results from real radar data demonstrate the effectiveness and feasibility of our method.
Conventional range-Doppler (RD) inverse synthetic aperture radar (ISAR) imaging method utilizes coherent integration of consecutive pulses to achieve high cross-range resolution. It requires the radar to keep track of the target during coherent processing intervals (CPI). This restricts the radar's multi-target imaging ability, especially when the targets appear simultaneously in different observing scenes. To solve this problem, this paper proposes a multi-target ISAR imaging method for phased-array radar (PAR) based on compressed sensing (CS). This method explores and exploits the agility of PAR without changing its structure. Firstly, the transmitted pulses are allocated randomly to different targets, and the ISAR image of each target can be then reconstructed from limited echoes using CS algorithm. A pulse allocation scheme is proposed based on the analysis of the target's size and rotation velocity, which can guarantee that every target gets enough pulses for effective CS imaging. Self-adaptive mechanism is utilized to improve the robustness of the pulse allocation method. Simulation results are presented to demonstrate the validity and feasibility of the proposed approach.
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