The number of optical true time delay (OTTD) is too many while operating in wideband and wide-angle condition for optically phased array radar. To solve the problem, a beam-forming method for optically phased array radar with sparse array is proposed. Firstly, the influence factor for main-lobe deviation is discussed. Secondly, a configuration of sparse antenna array is presented and then, on the basis of this, a beam-forming method based on Compressed Sensing theory is put forward. With this method, just a small amount of OTTD is required for wideband and wide-angle scanning. Finally, the effectiveness of this algorithm is validated by the simulative results.
KEYWORDS: Radar imaging, Synthetic aperture radar, Detection and tracking algorithms, Reconstruction algorithms, Signal to noise ratio, Target recognition, Scattering, Radar, Image compression, Monte Carlo methods
The inverse synthetic aperture radar (ISAR) imaging of high-speed targets is affected significantly by the phase modulation induced by the high-speed motion. To improve the imaging quality and efficiently suppress the influence of high-speed motion, a method of ISAR imaging via parametric sparse representation is proposed for high-speed targets. First, the echo is dynamically represented as a sparse signal via a flexible parametric sensing matrix according to the target high-speed motion. Subsequently, the sensing matrix is optimized through adaptive computation, during which the target velocity estimation is also achieved. Finally, the ISAR image of high-speed targets can be reconstructed with sparse sampling data. Compared to the existing method based on compressed sensing, the proposed method produces comparative imaging quality with less computational complexity and better robustness. Simulations are performed to validate the effectiveness of the method.
The amount of echo data is very large in highly squinted synthetic aperture radar (SAR) imaging with high resolution. To solve this problem, an imaging method for highly squinted SAR with under-sampled echo data based on compressed sensing (CS) is put forward. First, the echo signal model of highly squinted SAR is analyzed and a nonlinear chirp scaling (NCS) imaging method with the Nyquist-sampled echo data is proposed, with which the range walk is corrected and the range-azimuth coupling is mitigated. Based on the imaging method, the NCS operator is established. Combining the NCS operator and CS theory, a highly squinted SAR imaging scheme is formulated. The modified iterative thresholding algorithm is utilized to solve the imaging scheme, which forms a highly squinted SAR imaging method. With the proposed method, just a small amount of imaging data is required for highly squinted SAR imaging. Finally, the effectiveness of the proposed method is proven by the simulations.
Stepped-frequency waveforms (SFWs) can use the digital signal processing method to obtain high-range resolution with relatively narrow instantaneous bandwidth, which has been used in synthetic aperture radar (SAR). However, SFWs have the disadvantages of poor antijamming capability and a long period of transmission. Also, in the coherent integration time, some echo data are frequently lost. A two-dimensional sparse imaging method in the space and frequency domains for SAR is proposed based on compressed sensing (CS) theory. A sparse SFW for SAR imaging is formed and analyzed first, which has the advantages of better antijamming capability and a shorter time period of transmission. The range compression is completed by using CS theory. As to the sparse echo data in the space domain, the imaging operator and the CS-based imaging scheme are constructed to simultaneously implement the range cell migration correction and azimuth compression. Compared with the conventional SAR imaging method of SFWs, a much smaller number of frequencies and a smaller amount of imaging data are required for SAR imaging by using the proposed method. Finally, the effectiveness of the proposed method is proven by simulation and experimental results.