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This paper design waveform for high frequency radar to optimize its performances in co-channel interference/colored noise environment. Considering that radar applies the optimal filter for range processing, a novel objective function accounting for both Signal to Interference plus Noise Ratio (SINR) and Integrated Sidelobes Level (ISL) is introduced and the constant modulus constraint on the probing waveform is forced. To resolve the resulting non-convex quartic optimization problem, an efficient iterative algorithm based on Majorization-Minimization (MM) and Pattern Search (PS) is proposed. In each iteration, the proposed algorithm produces a multi-dimension quadratic majorized problem and then splits it into multiple one-dimension problems with closed-form solutions. Simulation results show that the SINR and ISL performances of designed waveform can be flexibly adjusted. Besides, the convergence speed and stability of the proposed algorithm are more satisfactory than the other methods.

In a synthetic aperture radar system, long-time coherent integration for ground moving targets suffers from the complex range migrations (RMs) and Doppler frequency migration (DFM), which lead to a serious degradation of the target detection. To address these problems and obtain the motion parameters of the moving target, a new algorithm based on modified location rotation transform (MLRT) and improved discrete chirp Fourier transform (IDCFT), i.e., MLRT-IDCFT, is proposed in this paper. More concretely, the matched filtering function is first utilized to remove the range curvature (RC) for improving the computational efficiency. After that, the MLRT-IDCFT corrects the residual linear range migration (LRM) through rotating coordinate locations of the echo trajectory and achieves the coherent integration as well as the motion parameter estimation via the IDCFT. The IDCFT exploits the prior information to adaptively adjust the parameter estimation range, which not only improves the parameter resolution but also guarantees the validity of the parameter estimation. Due to the linear transform property, the proposed method can work well under the low signal-to-noise ratio (SNR) environment. Additionally, it has a relatively low computational complexity since the exhaustive searching for acceleration is avoided. Several simulations are provided to demonstrate the effectiveness of the proposed algorithm.