Target imaging based on ℓ1ℓ0 norms homotopy sparse signal recovery and distributed MIMO antennas

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 ℓ₁ norm and ℓ₀ norm applicable to complex signals is proposed to recover the strong scatterers. This ℓ₁ norm and ℓ₀ norm homotopy method is then extended to the block sparse signal case. The equivalence between complex ℓ₁ ℓ₀ norms homotopy method and block size of two real ℓ₁ ℓ₀ 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 ℓ₀ norm based method (BSL0), spectral projected gradient (SPG L1), and block sparse Bayesian learning (BSBL). The signal recovery performance of ℓ₁ ℓ₀ 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.