KEYWORDS: Matrices, Image deconvolution, Detection and tracking algorithms, Scattering, 3D modeling, Synthetic aperture radar, Imaging arrays, Signal to noise ratio, Point spread functions, 3D image processing
A synthetic aperture radar three-dimensional imaging system based on frequency diversity array (3D-FDA-SAR) has the characteristics of low cost and flexible transmission signal and is used in 3D imaging of complex environments. However, due to the range-angle dependence of the frequency diversity array (FDA), the information in each dimension will be coupled with each other when imaging the target, and the space-time-frequency sparse characteristic of the echo signal leads to high side lobes in the imaging results. In this paper, the deconvolution algorithm is applied to the imaging of 3D-FDA-SAR, the coupling is removed according to the coupling generation characteristics, and the effect of reducing side lobes is achieved at the same time. Using MATLAB simulation and comparing the simulation results with the simulation using the BP algorithm directly, the results show that the 3D-FDA-SAR after adding the algorithm in this paper has a better effect on multi-target imaging and is more suitable for the real imaging environment.
The mapping of the radar echo dataset into a graph signal offers a novel perspective for solving the radar target localization problem. However, the published graph-based methods are mostly applicable to the uniform array configuration. In this paper, we propose an enhanced graph-based target localization method that can be applicable to the non-uniform frequency diversity array radar to fill this gap. Following the previous studies, we establish a space-domain graph model for the echo signal acquired from a non-uniform frequency diversity array radar. Subsequently, we employ the graph signal processing method to solve the target localization problem. Numerical simulations demonstrated that the proposed graph-based localization method provides a high resolution and accurate estimation, surpassing conventional methods.
Frequency diversity array (FDA) radar can automatically scan an area of interest without phase shifters by utilizing its range-angle-dependent beampattern, which is more convenient in terms of system implementation relative to the conventional phased array (PA) radar. However, the FDA cannot track a target continuously as the PA does because of the periodicity characteristic shown in the FDA’s beampattern. We address the problem of stable tracking of multiple moving targets, aiming at pursuing a high-resolution target imaging approach. First, we establish an inverse synthetic aperture radar (ISAR) imaging model applicable to multiple repeated subpulses based FDA ISAR (MRS-FDA-ISAR) radar. In the procedure of moving target imaging, the proposed MRS-FDA-ISAR scheme is able to not only avoid the problem of range-angle-coupling but also achieve high-level energy accumulation by compensating the phase of the target. Finally, the back projection algorithm is utilized to achieve high-resolution two-dimensional imaging of multiple moving targets. Numerical experiments demonstrated the effectiveness of the proposed approach and it is shown that this approach is superior to conventional ISAR imaging methods due to its high-level energy utilization and relatively low hardware overhead.
Aiming at the problem of limited resource allocation in netted radar system, this paper extends the idea of distributed multiple-input multiple-output (MIMO) radar to netted radar detection, and proposes a multi-target imaging resource scheduling algorithm for netted radar based on single-input multiple-output (SIMO) technology. The algorithm estimates the target size based on the target feature recognition, and uses the compressed sensing principle to calculate the pulse resources needed for target imaging. Secondly, the radar is selected according to the target size, and a reasonable resource scheduling model is established. Finally, the effectiveness of the algorithm is verified by simulation, and compared with the conventional netted radar algorithm, the scheduling success rate is improved and the consumption of pulse resources is reduced.
Deep learning method has been extensively applied to ground penetrating radar two-dimensional profile (GPR B-SCAN) hyperbola detection recently. We propose a B-SCAN image feature extraction method based on the constraints of the GPR physical model, and further detect the weak boundary feature curve of the target in the local space. A deep convolutional neural network (DCNN) is first designed to extract high-level semantic features from B-SCAN images to remove direct wave. Next, a multiscale feature fusion DCNN is used to extract the features of the B-SCAN image with the direct wave removed, and the classifier network is used to identify the hyperbola of the upper boundary feature of the target. Finally, according to the hyperbola, the local space corresponding to the target in the B-SCAN image is determined. On this basis, the amplitude and phase information of the scattered electric field are used to segment the lower boundary characteristic curve of the target through convolution operation. Experimental results on simulation and field data show that feature information of the buried target in the GPR B-SCAN image can be efficiently extracted when the proposed method is adopted.
A range-angle–dependent beam pattern can be produced by frequency diversity array (FDA) due to the small frequency offsets between the array elements. The beam pattern can be used to automatically scan an area in its entirety and estimate the distance and angle to a target. However, for constant tracking of the target after recognition, energy is wasted in the scanning mode because of periodic scanning of the main lobe of the beam. To eliminate this energy waste, we propose multiple repeated subpulses of FDA. This scheme achieves stable tracking without range–angle coupling. This special transmission method considerably improves the transmission energy and signal-to-noise ratio, while ensuring accurate range detection and high resolution. The results of a feasibility analysis and simulation experiments verify the superiority of the proposed method.
Aiming at optimizing the allocation problem of limited resources in a radar network, a resource scheduling algorithm combining pulse interleaving with preallocation is proposed for multitarget inverse synthetic aperture radar imaging. The imaging method adopts compressed sensing, which only needs to emit a small number of pulses so that we can set the algorithms to schedule the allocation of the pulses over a period of time. The authors point out the problem of pulse conflict, which is ignored in the process of the scheduling algorithm and proposes a preallocation method to avoid the occurrence of the conflict. Meanwhile, pulse interleaving is added to increase the positions of the dispatchable pulses. Moreover, the combined algorithm can perform adaptive scheduling on the radar time resource according to the feature parameters after target feature cognitive. Finally, the feasibility of the combined algorithm is verified by the simulation, and two performance indicators, the hit value rate and the pulse utilization rate, are improved by the proposed algorithm.
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