Analysis Of Signal Processing Algorithms Research Articles

Performance analysis of signal processing algorithms for wall clutter mitigation and contrast target detection

AbstractThe presence of clutter in through‐the‐wall images significantly degrades image quality and hampers the performance of algorithms used for data processing in tasks such as target detection, identification, or reconstruction. Real building walls exhibit inherent inhomogeneities, featuring varying frequency and spatial properties that defy the assumption of a smooth surface. Consequently, they produce nonuniform wall surface reflections at each scanning position. Furthermore, these walls often incorporate supply pipes that introduce substantial clutter, which, although stronger than the target responses, remains weaker than the wall surface clutter. Additionally, this clutter often exhibits signatures similar to those of the target. In real‐world scenarios, target reflections can manifest as wide, flat hyperbolas or nearly straight lines, and they may be positioned near the wall. Consequently, distinguishing targets from clutter becomes a complex challenge. Various clutter reduction methods have been proposed in recent years, showing varying degrees of success. However, the effectiveness of these methods in the literature is inconsistent and sometimes contradictory. To address this issue, a comprehensive investigation of well‐established clutter reduction methods was conducted to evaluate their performance under identical conditions. These methods were rigorously assessed using practical radar‐measured data acquired in the presence of actual building wall materials and contrasting targets. Evaluation criteria include the target‐to‐clutter ratio and peak signal‐to‐noise ratio. The results of the experiment revealed that independent component analysis outperformed other clutter reduction methods, demonstrating superior performance in mitigating clutter and enhancing target detection.

PhysFad: Physics-Based End-to-End Channel Modeling of RIS-Parametrized Environments With Adjustable Fading

Programmable radio environments parametrized by reconfigurable intelligent surfaces (RISs) are emerging as a new wireless communications paradigm, but currently used channel models for the design and analysis of signal-processing algorithms cannot include fading in a manner that is faithful to the underlying wave physics. To overcome this roadblock, we introduce a physics-based end-to-end model of RIS-parametrized wireless channels with adjustable fading (coined PhysFad) which is based on a first-principles coupled-dipole formalism. PhysFad naturally incorporates the notions of space and causality, dispersion (i.e., frequency selectivity) and the intertwinement of each RIS element’s phase and amplitude response, as well as any arising mutual coupling effects including long-range mesoscopic correlations. The latter are induced by reverberation and yield a highly nonlinear parametrization of wireless channels through RISs, a pivotal property which is to date completely overlooked. PhysFad offers the to-date missing tuning knob for physics-compliant adjustable fading. We thoroughly characterize PhysFad and demonstrate its capabilities for a prototypical problem of RIS-enabled over-the-air channel equalization in rich-scattering wireless communications. We also share a user-friendly version of our code to help the community transition towards physics-based models with adjustable fading.

Application of random matrix theory to acoustic modeling and signal processing

Random matrix theory (RMT) characterizes the eigenvalues and eigenvectors of matrices composed of random entries [Bai/Silverstein, Springer, 2010]. Random matrices play an important role in a variety of acoustics and signal processing applications. For example, mode propagation through internal waves can modeled using random scattering matrices Hegewisch and Tomsovic showed that mode scattering can be analyzed using RMT [JASA, 2013]. In signal processing, the sample covariance matrix is an example of a random matrix that can be analyzed using RMT techniques. RMT differs from classical statistics because it derives results in the limit as both array size and number of measurements approach infinity. This type of asymptotic analysis is crucial because it facilitates the investigation of large dimensional matrices in scenarios with limited numbers of measurements. While results are derived in the infinite limit, many authors have shown that RMT provides useful predictions for finite size matrices derived from finite numbers of measurements. Research in RMT has grown substantially since the 1950s, and new theoretical results are rapidly emerging. This talk reviews several key results from the RMT literature and provides examples to illustrate the analysis of signal processing algorithms and acoustic propagation using these powerful methods.

Analysis of Signal Processing Algorithms of Coriolis Mass Flowmeters

For fast and precise flow measurement using coriolis flowmeters it is necessary to accurately determinate the phase difference between two noisy position signals. These signals are digitized and filtered before being processed by phase tracking algorithms. This work investigates the influence of discretization and noise shaping on different phase tracking methods and their estimation efficiency, computational complexity, real-time performance and memory requirements.