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Abstract
The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate the background and the foreground in the trend of UWB Radar-based moving target detection. Robust PCA models are criticized for being batched-data-oriented, which makes them inconvenient in realistic environments where frames need to be processed as they are recorded in real time. In this paper, a novel method based on overlapping-windows processing is proposed to cope with online processing. The method consists of processing a small batch of frames which will be continually updated without changing its size as new frames are captured. We prove that RPCA (via its Inexact Augmented Lagrange Multiplier (IALM) model) can successfully separate the two subspaces, which enhances the accuracy of target detection. The overlapping-windows processing method converges on the optimal solution with its batch counterpart (i.e., processing batched data with RPCA), and both methods prove the robustness and efficiency of the RPCA over the classic PCA and the commonly used exponential averaging method.
Highlights
In recent years, Ultra-Wideband (UWB) radars have attracted a large amount of interest in research due to their wide variety of practical applications and their ability to operate in indoor environments and areas with poor visibility conditions
This paper considers 2 radar modules, each with a mono-static UWB radar configuration where waveform pulses are transmitted from a single transmitting antenna and the scattered waveforms are received by a collocated receiving antenna
With the intention to illuminate the differences in performance of the discussed background techniques, their respective outputs were passed to the CLEAN algorithm for target detection and the obtained results are compared
Summary
Ultra-Wideband (UWB) radars have attracted a large amount of interest in research due to their wide variety of practical applications and their ability to operate in indoor environments and areas with poor visibility conditions. Compared to its batch-counterpart where the adopted nuclear norm tightly couples the samples and the samples have to be processed simultaneously, OR-PCA pursues the low-rank component in a different manner: using an equivalent form of the nuclear norm, OR-PCA explicitly decomposes the sample matrix into the multiplication of the subspace basis and coefficients plus a sparse noise component Through such decomposition, the samples are decoupled in the optimization and can be processed separately. The most relatively related to our approach in techniques is [12], where a compressive sensing based recursive robust PCA algorithm is proposed Their proposed method essentially solves compressive sensing optimization over a small batch of data to update the principal components estimation. Their algorithm guaranteed a good performance only on data with structured noise which is too restrictive in real life scenarios. For the stochastic OR-PCA algorithm, the average computational time for one sample of length 240 is 0.0748 s, and 0.0724 s for GRASTA [15]
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