Abstract
Chaos through-wall imaging radar has attracted wide attention due to its inherent low probability of detection/interception, strong anti-jamming, and high resolution. However, the target response is usually overwhelmed by strong clutter. This paper proposes an imaging-then-decomposition method based on two-stage robust principal component analysis (RPCA) to remove the clutter and recover the target image. The proposed method firstly focuses the energy of the preprocessing data by the back-projection imaging algorithm; then, it performs matrix decomposition on the full and the sparse component of the focused data, in succession, by the RPCA algorithm. Simulation and experimental results show that the proposed method can suppress the clutter dramatically and indicate human targets distinctly. Compared with the traditional methods, it has effectiveness and superiority in improving the signal-to-clutter ratio.
Highlights
Through-wall imaging (TWI) radar plays an important role in both military and civil applications, such as urban warfare, antiterrorism, homeland security, law enforcement, and disaster survivor detection [1]
In this paper, motivated by the success of the robust principal component analysis (RPCA), an imaging--decomposition clutter suppression method based on two-stage RPCA is proposed for chaos TWI radar
It is worth noting that many variants of Equation (2) have been proposed with the goal being either lower complexity or better performance, but we just use the original one, since we focus on the utilization of the RPCA-based method in chaos TWI radar
Summary
Through-wall imaging (TWI) radar plays an important role in both military and civil applications, such as urban warfare, antiterrorism, homeland security, law enforcement, and disaster survivor detection [1]. Tang et al [22] presented a joint low-rank and sparsity-based method for compressed sensing TWI radar, which can mitigate the wall reflection and reconstruct an image of the scene even when the number of measurements is significantly reduced. Cross-correlation between the echo signal and the reference signal is necessary to determine the position of the reflection events, which results in the target reflections appearing as wide and flat hyperbolas, which are sometimes nearly straight lines In this case, the direct RPCA method performs insufficiently due to the degradation of the sparsity of the target responses. Unlike [27], where RPCA is used once with a general regularization parameter, the proposed method applies RPCA twice and optimizes the regularization parameter to balance the low-rank and sparsity components and to enhance the target detection performance for chaos TWI radar.
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