Temporal-spatial Stochastic Radiation Field Research Articles

Waveform design of radar coincidence imaging radiation field based on image entropy

AbstractRadar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid.

Stochastic radiation field optimization for microwave staring correlated imaging via spatial correlation minimization

Microwave staring correlated imaging (MSCI) is a staring high-resolution microwave imaging technique, employing the temporal–spatial stochastic radiation field (TSSRF). The imaging capability of MSCI depends on the spatial correlation of the TSSRF, which is equivalent to the incoherence of the sensing matrix in discrete form. A waveform design method for MSCI using multifrequency signals to reduce the spatial correlation of the TSSRF is proposed. The amplitude of each frequency component in the multifrequency signals serves as the design variable. Considering the power and the amplitude constraints in the practical radar system, we first form the waveform design as a constrained optimization problem. Then, it is reformulated as a nonsmooth unconstrained optimization problem by the penalty function method. With the smoothing approximation, the reformulated problem is solved by alternating minimization. Numerical experiments show that the proposed method can decrease the spatial correlation of the TSSRF and improve the imaging performance of MSCI as a result.

A Self-Calibration Imaging Method for Microwave Staring Correlated Imaging Radar With Time Synchronization Errors

Microwave Staring Correlated Imaging (MSCI) technology has attracted increasing attentions for its ability to acquire high-resolution images in staring imaging geometry. The key point of MSCI is to construct the temporal-spatial stochastic radiation field which can be realized by multi-transmitter systems emitting frequency-hopping (FH) waveforms. However, time synchronization errors always exist in practice, which heavily deteriorates the imaging results. Therefore in this article, a two-step self-calibration imaging method for MSCI transmitting frequency-hopping waveforms is proposed to directly estimate the time synchronization errors. At the first step, a sparsity driven algorithm is proposed which utilizes a novel cluster sparse prior to improve the imaging performance. At the second step, a self-calibration operation is proposed the first time to directly compensate the time synchronization errors in MSCI. By alternately reconstructing the targets and estimating the time synchronization errors, the proposed method can reconstruct the target with high imaging quality and effectively compensate the time synchronization errors. Numerical simulations demonstrate the potential advantages of the proposed method to enhance the imaging quality and improve the time synchronization error estimation performance.

Autofocus technique for radar coincidence imaging with model error via iterative maximum a posteriori

Radar coincidence imaging (RCI) is a recently developed staring imaging technique based on the wavefront random modulation and temporal–spatial stochastic radiation field. Before coincidence imaging processing, the RCI needs to compute the reference matrix accurately. Unfortunately, model error usually exists, which degrades the imaging performance considerably. In this article, by exploiting the sparse prior of target, the authors propose a sparsity-driven autofocus imaging via iterative maximum a posteriori (SA-MAP) algorithm for the RCI when model error exists. The algorithm performs well via simultaneous sparse imaging, self-calibration and parameters update. Simulation results demonstrate the validity of the proposed algorithm and performance improvement over the existing algorithms.

Adaptive Microwave Staring Correlated Imaging for Targets Appearing in Discrete Clusters.

Microwave staring correlated imaging (MSCI) can achieve ultra-high resolution in real aperture staring radar imaging using the correlated imaging process (CIP) under all-weather and all-day circumstances. The CIP must combine the received echo signal with the temporal-spatial stochastic radiation field. However, a precondition of the CIP is that the continuous imaging region must be discretized to a fine grid, and the measurement matrix should be accurately computed, which makes the imaging process highly complex when the MSCI system observes a wide area. This paper proposes an adaptive imaging approach for the targets in discrete clusters to reduce the complexity of the CIP. The approach is divided into two main stages. First, as discrete clustered targets are distributed in different range strips in the imaging region, the transmitters of the MSCI emit narrow-pulse waveforms to separate the echoes of the targets in different strips in the time domain; using spectral entropy, a modified method robust against noise is put forward to detect the echoes of the discrete clustered targets, based on which the strips with targets can be adaptively located. Second, in a strip with targets, the matched filter reconstruction algorithm is used to locate the regions with targets, and only the regions of interest are discretized to a fine grid; sparse recovery is used, and the band exclusion is used to maintain the non-correlation of the dictionary. Simulation results are presented to demonstrate that the proposed approach can accurately and adaptively locate the regions with targets and obtain high-quality reconstructed images.

Waveform Analysis and Optimization for Radar Coincidence Imaging with Modeling Error

RCI is a novel superresolution staring imaging technique based on the idea of wavefront modulation and temporal-spatial stochastic radiation field. For RCI, the reference matrix should be known accurately, and the imaging performance depends on the incoherence property of the reference matrix. Unfortunately, the modeling error, which degrades the performance significantly, exists generally. In this paper, RCI using frequency-hopping waveforms (FH-RCI) is considered, and a FH code design method aiming to increase the robustness of RCI to modeling error is proposed. First, we derive the upper bound of imaging error for RCI with modeling error and conclude that the condition number of the reference matrix determines the imaging performance. Then the object function for waveform design which minimizes the condition number of the reference matrix is achieved, and the quantum simulated annealing (QSA) is employed to optimize the FH code. Numerical simulations show that the optimized FH code could decrease the condition number of the reference matrix and improve the imaging performance of RCI with modeling error.

A novel super-resolution imaging method based on stochastic radiation radar array

A novel concept of stochastic radiation radar array (SRRA) is proposed to achieve super-resolution in real aperture staring radar imagery. Firstly, the definition of the temporal–spatial stochastic characteristics of the temporal–spatial stochastic radiation field (TSSRF) is presented. Then, a correlated imaging algorithm based on Gram–Schmidt orthogonalization (GSO-CIA) is proposed to reconstruct the target image using the echo signals and TSSRF. By the theoretical analysis, the super-resolution potentiality of staring imaging based on stochastic radiation radar array (SRRA-SI) is verified, which can overcome the resolution limitation of the real antenna aperture. The influences of the random transmitting signals and the configuration of the radar array (the number of array elements, the size array aperture, etc) on the stochastic characteristics of TSSRF are investigated in detail. Finally, numerical simulations are performed and the striking effect of super-resolution, which is more than 20 times compared with traditional real-aperture radar array, is demonstrated.