Radar detection and fast motion parameter estimation for complex manoeuvering targets at high speed and acceleration

Abstract

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.

Long‐time coherent integration for radar detection of manoeuvring targets based on accurate range evolution model

“…In most of the existing LTCI methods [22][23][24][25][26][27][28][29][30][31][32][34][35][36][37][38][39][40][41][42][43] dealing with manoeuvring targets, the target range model r(t m ) is usually formulated as polynomials with high order. In general, these polynomial models may only strictly match the targets moving along the radar line of sight (see Figure 1a).…”

“…The LTCI methods mentioned above [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][34][35][36][37][38][39][40][41][42][43] all assume that the target motion satisfies polynomial models in range coordinates. In reality, however, a few motions could be accurately matched by polynomial models.…”

“…Nevertheless, the cross‐terms generated by the non‐linear transform may make the ACCF‐based methods not suitable for energy accumulation under low SNR environment. Some other typical methods, such as series reversion based method [38], KT with matched filtering (KTMF) [39], KT with generalised dechirp process (KTGDP) [40], generalised KT with generalised dechirp process (GKTGDP) [41], non‐uniform resampling and scale processing (NUSP) technique [42] and geometric‐auxiliary LRT with periodic scaled generalised high‐order ambiguity function (GLRT‐PSGHAF) [43], are developed to make a balance between accumulation performance and computational complexity.…”

“…The LTCI methods mentioned above [13–32, 34–43] all assume that the target motion satisfies polynomial models in range coordinates. In reality, however, a few motions could be accurately matched by polynomial models.…”

A Novel Coherent Integration Algorithm for Maneuvering Target Detection Based on Symmetric Instantaneous Autocorrelation Function

“…Due to the feasible low-radar cross-section of maneuvering targets, the returned radar signal can be quite weak, which can make the detection more difficult. With the help of long-time CI techniques, the MaT detection performance of radar can be significantly improved [6][7][8][9]. However, target maneuvering can cause high-order range migration (RM) and Doppler frequency migration (DFM), which creates considerable challenges for CI.…”