4/2/2023 0 Comments Dsp net radarThe implementations were tested using a dataset from a previous experiment of the authors. Compared to the NVIDIA GeForce GT 650 M implementation, the NVIDIA GeForce GTX 660 Ti achieves a speedup of 4.0 and the NVIDIA Tesla K20c a speedup of 6.2. Compared to a C programming language CPU-based implementation, the NVIDIA GeForce GT 650 M achieves a speedup of 4.9, the NVIDIA GeForce GTX 660 Ti achieves a speedup of 19.5 and the NVIDIA Tesla K20c a speedup of 30.2. The implementation was tested on a NVIDIA GeForce GT 650 M, on a NVIDIA GeForce GTX 660 Ti, and a NVIDIA Tesla K20c. The pixel calculation is calculated using the GPU accelerator, implementing the operations presented in Equation ( 28). CUDA is used to program the GPU, mainly the NVIDIA CUDA FFT library (cuFFT) and the complex vector operations available in the NVIDIA CUDA Basic Linear Algebra Subroutines (cuBLAS) library. The difference between these two images, represented by the SSIM value, is 0.095399.Īn implementation of the backprojection algorithm for frequency-modulated continuous wave (FMCW) SAR tested on three different devices is presented in. A fairer comparison is between the matched-filter algorithm generated image, Figure 10b, and the fast-factorized backprojection generated image, Figure 10c. As for the special case of the GMTI image, the SSIM values are so different due to differences in the algorithms, with the backprojection creating dark triangles in the corners while the other two algorithms generate an extremely unfocused region. However, it may satisfy the quality requirements for some applications, with its lower execution times. The fast-factorized algorithm falls behind when it comes to image quality, with a difference in the SSIM values between 0.04 and 0.14. This is an expected outcome, since these algorithms generate high-quality images, and the backprojection is able to maintain a quality similar to the matched filter algorithm with a smaller execution time. The SSIM values obtained when comparing the images generated using the backprojection algorithm and the matched filter are close to 1, with a difference up to 0.004. Every image and test presented in this review was executed on a PC desktop with a quad-core Intel Core i7-9700F processor, a NVIDIA GeForce RTX 2060 GPU, 32GB of RAM and 1TB of SSD. Fifty degrees of azimuth takes around 5 hours, and for the GMTI dataset, almost 7 hours. For this reason, it was not possible to generate larger images using this algorithm. From this table, it is possible to observe that this algorithm takes between 53 and 517 times longer than any of the others, backprojection algorithm and fast-factorized backprojection. The execution times of the formation of these images are given by Table 5. The images generated by the matched filter algorithm using the GOTCHA Volumetric dataset are presented in Figure 9, the one generated using the GMTI Challenge problem in Figure 10b and the synthetic point target one in Figure 11b. For higher-level products, digital elevation models are required when processing SAR data, such as the radiometric terrain correction (RTC) and interferometric SAR (InSAR) products.įrom the algorithms tested in this review, the matched filter algorithm has the highest computational complexity, O ( n 4 ), making it impractical for most applications. Table 3 summarizes the main advantages and disadvantages of these algorithms in regard to Level 0 products. The matched filter and the backprojection algorithms are both time-domain algorithms and their computational complexity is superior, however, the images do not suffer from the same warping as the previously mentioned algorithms. Of the algorithms presented here, Range–Doppler algorithm, chirp scaling algorithm, omega-K algorithm, and polar format algorithm are frequency-domain algorithms. The backprojection algorithm only performs a range FFT, while the polar format algorithm performs a range and an azimuth FFT, which introduces the side lobes. However, the main drawback of such a method is the introduction of side lobes and unfocused regions as the distance to the scene center increases. Most of the algorithms presented here are frequency-domain algorithms, which also means that they usually have higher computing efficiency.
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