Conventional inverse synthetic aperture radar observes the target from one viewing direction and only obtains partial information. By using a distributed multiple-input multiple-output (MIMO) array, the target can be observed from multiple views and a better image can be obtained. The distributed MIMO radar imaging signal model is derived in this paper. Because the strong scatterers (scattering centers) of a target are usually sparsely distributed, a sparse signal recovery algorithm using homotopy between the l 1  norm and l 0  norm applicable to complex signals is proposed to recover the strong scatterers. This l 1  norm and l 0  norm homotopy method is then extended to the block sparse signal case. The equivalence between complex l 1  l 0  norms homotopy method and block size of two real l 1  l 0  norms homotopy method is demonstrated. This proves that a complex signal-based algorithm is better than a real signal-based algorithm, which only separates the complex signal into its real part and imagery part and does not use their block property. This method is compared with other methods, i.e., block orthogonal matching pursuit (BOMP), block CoSaMp, block smoothed l 0  norm based method (BSL0), spectral projected gradient (SPG L1), and block sparse Bayesian learning (BSBL). The signal recovery performance of l 1  l 0  norms homotopy method is better than the other methods except for BSBL. However, BSBL costs more computationally. Imaging using distributed MIMO antennas is simulated. The simulation results show that the image quality using distributed MIMO radar is better than that using monostatic (or bistatic) MIMO radar.
['Changzheng Ma', 'Tat Soon Yeo', 'Zhoufeng Liu', 'Qun Zhang', 'Qiang Guo']
Target imaging based on ℓ 1 ℓ 0 norms homotopy sparse signal recovery and distributed MIMO antennas