We focus on the range migration (RM) and Doppler frequency migration (DFM) corrections in the long-time coherent integration, and a fast detection method based on two-dimensional trilinear autocorrelation function is proposed for the maneuvering target with jerk motion. This proposed method can integrate the echoes’ energy into peaks in a three-dimensional parameter space coherently and estimate the target’s radial range, acceleration, and jerk simultaneously by the peak detection technique. Then through the estimations of radial range, acceleration, and jerk, the radial velocity can be obtained through one-dimensional parameter searching. Finally, RM and DFM can be compensated simultaneously, and the target can be detected through the constant false alarm technique. This proposed method can strike a good balance between the computational complexity and detection performance. Experiments with the simulation and real measured radar data are conducted to verify the proposed method.
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.
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.
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