Coherent integration of complex manoeuvering targets at high speed and acceleration causes range and Doppler frequency migration and lead to large computational burdens and parameter estimation error. To solve this, the geometric-auxiliary location rotation transform (GLRT), fast parameter estimation method, and periodic, scaled generalised, high-order ambiguity function (PSGHAF) were proposed for this study. The quadratic range migration was corrected using the second-order keystone transform. The GLRT uses the geometrical relationship between the rotation angle and trajectory projection to estimate the velocity and initial range in different noise environments without searching for multidimensional parameters. Thereafter, the high-acceleration and jerk estimations of the target signal are obtained using PSGHAF, which uses the periodicity of discrete Fourier transformation (DFT) to extend the estimation scope of acceleration. This avoids estimation errors without changing the radar system parameters. Compared with others, the proposed algorithm has a lower computational complexity and improved detection performance. Numerical simulations and real data from unmanned aerial vehicles demonstrate the efficacy of the proposed solution. K E Y W O R D Sgeometric-auxiliary location rotation transform, long-time coherent integration, periodic scaled generalised high-order ambiguity function, range migrationThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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For the application of near range imaging with wideband LFMCW-SAR imaging, this paper presents a scheme to generate X-band signal with 5.1 GHz bandwidth based on a YIG oscillator. This method has a simple structure and low cost. However, it introduces a certain degree of nonlinearity, resulting in range-resolution degradation. Conventional nonlinearity estimation methods do not work well or fail with low SINR. To solve this problem, a two-step high order ambiguity function method, based on wide and narrow band filters, is used to estimate the nonlinear error. The nonlinearity is corrected by resampling. In the end, the nonlinear correction algorithm is validated with simulation data, delay-line data, and rail-SAR data. Keywords linear frequency modulated continuous wave synthetic aperture radar (LFMCW-SAR), YIG oscillator, high order ambiguity function (HAF), nonlinearity correction, two-step correction imaging Tao LAI obtained M.S. degree and Ph.D. degrees in information and communication engineering from Na