It is well known that long time coherent integration (LTCI) can
effectively improve the radar detection ability of manoeuvring weak
targets, since a considerable signal-to-noise ratio (SNR) improvement
can be achieved [1]. However, for most existing LTCI algorithms
[2-5], there is a common assumption that the observed target is of
the single motion stage (i.e., the motion parameters of targets are
uniform) during the coherent processing interval (CPI). However, with
the advancement of manoeuvrability and the increasement of CPI, the
observed target might be of multiple motion stages. In this case, the
above-mentioned LTCI algorithms will not be effective any more. The
specific LTCI algorithms developed for manoeuvring weak target with
multiple motion stages are relatively few. In [6], a short-time
generalized radon-Fourier transform (STGRFT) based LTCI algorithm is
proposed to remove range migration (RM) and Doppler frequency migration
(DFM) effects and estimate the stage-changing point. Similar as
GRFT[5], STGRFT can be able to obtain an excellent SNR gain through
multi-dimension parametric searching. In [7], a reference signal is
introduced to compensate the motion parameters change (MPC) effect
between different motion stages, and then GRFT is utilized to achieve
the coherent integration during the CPI. However, the computational load
of these algorithms is quite high, since the key procedure is based on
the multi-dimension parametric searching. This may deteriorate the
engineering practicability of these algorithms.
Bayesian compressive sensing (BCS) is an important sub-class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub-optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex-valued signal processing field.
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