Abstract
The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Filter (MF) cannot enjoy good imaging performance owing to the few and incomplete wavenumber-domain coverage. Higher resolution and better imaging performance can be obtained by exploiting the sparsity of the target. However, good sparse recovery performance is based on the assumption that the scatterers of the target are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. Here, we propose a novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general off-grid FD-MIMO radar imaging. SACR-iMAP contains three loop stages: sparse recovery, off-grid errors calibration and parameter update. The convergence and the initialization of the method are also discussed. Numerical simulations are carried out to verify the effectiveness of the proposed method.
Highlights
The multiple-input-multiple-output (MIMO) radar system has attracted much attention recently, due to the additional degrees of freedom and the higher spatial resolution [1]
We propose an approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the off-grid frequency diverse multiple-input-multiple-output (FD-MIMO) radar imaging
Considering that the linear frequency modulated (LFM) signal has long been used in radar systems, because of its implementation simplicity, constant modulus and high range resolution [5], here, we focus our derivation on the LFM-based FD-MIMO radar imaging
Summary
The multiple-input-multiple-output (MIMO) radar system has attracted much attention recently, due to the additional degrees of freedom and the higher spatial resolution [1]. In MIMO radar imaging [6,9], since the scatterers are distributed in a continuous scene, the off-grid problem usually emerges, even if the discretized grid is dense, which would lead to the mismatch of the sensing matrix. We propose an approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the off-grid FD-MIMO radar imaging. Taylor approximation and establish an alternating process to obtain the optimized recovery results and off-grid errors estimation Since they have not deduced the imaging problem from the Bayesian maximum a posteriori aspect, the estimation of the power of noise is unavailable. T is the Hadamard (elementwise) and vec (⋅) denote the transpose, the conjugate transpose operation and the vectorization operation, respectively
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