Aperture Radar Imaging Of Targets Research Articles

Synthetic Aperture Radar Image Target Recognition Based on Updated Classifier

Synthetic Aperture Radar Image Target Recognition Based on Updated Classifier

Deep convolutional neural network structural design for synthetic aperture radar image target recognition based on incomplete training data and displacement insensitivity

Deep convolutional neural networks (DCNN) are extensively used in image classification. However, it is typically impossible to receive ideal effects if directly using it in synthetic aperture radar (SAR) image target recognition. During the data acquisition process, it is difficult to acquire a large number of labeled training data and to ensure the image targets in the center. To extract the width and height features of SAR images and reduce the computational complexity, we constructed an asymmetric parallel convolution module. The module avoids severe over-fitting due to limited training samples and effectively deals with displacement changes with test samples. Meanwhile, the residual learning method is used in the algorithm to avoid the deep network degradation and improve algorithm recognition accuracy (RA). Experimental results show that the RA of the DCNN with residual-learning-based APCRLNet reaches 99.75% under standard operating conditions, which is superior to existing recognition methods. Furthermore, the algorithm also performs well for incomplete training samples and test samples with displacement changes.

Inverse Synthetic Aperture Radar Imaging of Targets with Complex Motion based on Optimized Non-Uniform Rotation Transform

Focusing on the inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, this paper proposes a modified version of the Fourier transform, called non-uniform rotation transform, to achieve cross-range compression. After translational motion compensation, the target’s complex motion is converted into non-uniform rotation. We define two variables—relative angular acceleration (RAA) and relative angular jerk (RAJ)—to describe the rotational nonuniformity. With the estimated RAA and RAJ, rotational nonuniformity compensation is carried out in the non-uniform rotation transform matrix, after which, a focused ISAR image can be obtained. Moreover, considering the possible deviation of RAA and RAJ, we design an optimization scheme to obtain the optimal RAA and RAJ according to the optimal quality of the ISAR image. Consequently, the ISAR imaging of targets with complex motion is converted into a parameter optimization problem in which a stable and clear ISAR image is guaranteed. Compared to precedent imaging methods, the new method achieves better imaging results with a reasonable computational cost. Experimental results verify the effectiveness and advantages of the proposed algorithm.

Inverse synthetic aperture radar imaging of targets with complex motion based on the local polynomial ambiguity function

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, the azimuth echoes have to be modeled as multicomponent cubic phase signals (CPSs) after motion compensation. For the CPS model, the chirp rate and the quadratic chirp rate deteriorate the ISAR image quality due to the Doppler frequency shift; thus, an effective parameter estimation algorithm is required. This paper focuses on a parameter estimation algorithm for multicomponent CPSs based on the local polynomial ambiguity function (LPAF), which is simple and can be easily implemented via the complex multiplication and fast Fourier transform. Compared with the existing parameter estimation algorithm for CPS, the proposed algorithm can achieve a better compromise between performance and computational complexity. Then, the high-quality ISAR image can be obtained by the proposed LPAF-based ISAR imaging algorithm. The results of the simulated data demonstrate the effectiveness of the proposed algorithm.

Errata: Inverse synthetic aperture radar imaging of targets with complex motions based on modified chirp rate–quadratic chirp rate distribution for cubic phase signal

The <i>Journal of Applied Remote Sensing</i> (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.

Inverse synthetic aperture radar imaging of targets with complex motions based on modified chirp rate–quadratic chirp rate distribution for cubic phase signal

For targets with complex motions, the time-varying Doppler frequency deteriorates inverse synthetic aperture radar (ISAR) images. After range alignment and phase adjustment, azimuth echoes in a range cell can be modeled as multicomponent cubic phase signals (CPSs). The chirp rate and the quadratic chirp rate of the CPS are identified as the causes of the time-varying Doppler frequency; thus, it is necessary to estimate these two parameters correctly to obtain a well-focused ISAR image. The parameter-estimation algorithm based on the modified chirp rate-quadratic chirp rate distribution (M-CRQCRD) is proposed for the CPS and applied to the ISAR imaging of targets with complex motions. The computational cost of M-CRQCRD is low, because it can be implemented by the fast Fourier transform (FFT) and the nonuniform FFT easily. Compared to two representative parameter-estimation algorithms, the M-CRQCRD can acquire a higher antinoise performance due to the introduction of an optimal lag-time. Through simulations and analyses for the synthetic radar data, the effectiveness of the M-CRQCRD and the imaging algorithm based on the M-CRQCRD are verified.

Inverse synthetic aperture radar imaging of targets with nonsevere maneuverability based on the centroid frequency chirp rate distribution

For inverse synthetic aperture radar (ISAR) imaging of targets with nonsevere maneuverability, the Doppler frequencies of scatterers are actually time-varying and azimuth echoes of a range cell have to be modeled as multicomponent linear frequency modulation (LFM) signals after the range alignment and the phase adjustment. In ISAR imaging with the LFM signal model, the chirp rate deteriorates the target image and an effective parameter estimation algorithm is required. By employing a symmetric instantaneous self-correlation function and the modified scaled Fourier transform, an effective parameter estimation algorithm, known as the centroid frequency chirp rate distribution (CFCRD), is proposed and applied to ISAR imaging. Compared to two representative parameter estimation algorithms, the modified Wigner-Ville distribution and the Lv’s distribution, the proposed CFCRD can acquire a higher antinoise performance without spectrum aliasing and brute-force searching. Through simulations and analyses of the synthetic radar data and the real radar data, we verify the effectiveness of CFCRD and the corresponding ISAR imaging algorithm.

Inverse synthetic aperture radar imaging of targets with complex motion based on cubic Chirplet decomposition

High resolution inverse synthetic aperture radar (ISAR) imaging of targets with complex motion is a main topic in the radar imaging domain. In fact, the traditional range-Doppler algorithm is not appropriate to generate a focused ISAR images because of the time-varying Doppler shifts caused by the target's complex motion. In this study, the azimuth received signal is modelled as multi-component amplitude-modulated and frequency-modulated (AM–FM) signal, and a novel algorithm for the cubic Chirplet decomposition based on generalised cubic phase function is proposed to investigate the AM–FM signal analytically. Then, the corresponding ISAR imaging algorithm associated with the range-instantaneous-Doppler technique is proposed. Results of simulated and real data demonstrate the effectiveness of the presented algorithm.