Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these dis...
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Mitigating automotive radar-to-radar interference is a challenging task, especially when the observed signal is densely corrupted with highly correlated interference signals. In this paper, we propose to remove this i...
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ISBN:
(数字)9798350374513
ISBN:
(纸本)9798350374520
Mitigating automotive radar-to-radar interference is a challenging task, especially when the observed signal is densely corrupted with highly correlated interference signals. In this paper, we propose to remove this interference using joint-conditional posterior sampling with score-based diffusion models. These models use three individual scores: a target score, an interference score, and a joint data consistency score. Leveraging the sparsity of clean target signals in the Fourier domain, we propose a model-based score estimator for the target signals, derived from the proximal step of the ℓ
1
-norm. For the interference score, we use a neural network with denoising score-matching, given that it is difficult to obtain analytical statistical models of the interference signals. Lastly, the target and interference scores are connected by a data-consistency score. Experimental results show that our solution results in superior performance over state-of-the-art methods, in terms of normalized mean squared error (NMSE) and receiver operating characteristic (ROC) curves.
Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for invers...
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Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these dis...
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Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these distortions by utilizing data from one dimension to reconstruct the corrupted samples, which do not perform well when a large number of samples are interfered and have difficulty recovering weak target *** this paper, model-based deep learning interference mitigation algorithms, called ALISTA and ALFISTA, are presented that reduce these artifacts by leveraging the full integration gain using all uncorrupted fast-time and slow-time samples. Simulations with 50% corrupted samples show that target peak power loss and velocity peak-to-sidelobe ratio (VPSR) with a 20-layer ALFISTA improves with 5.5 and 9.6 dB compared to zeroing. Furthermore, significant improvements in precision and recall are observed, even when large amounts (50-80%) of samples are missing.
The performance of two-way full-duplex communications with the help of reconfigurable intelligent surface (RIS) under phase noise is investigated in this work. Particularly, we study the outage probability, the averag...
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A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. ...
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Real-time or applied digital signalprocessing courses are offered as follow-ups to conventional or theory-oriented digital signalprocessing courses in many engineering programs for the purpose of teaching students t...
ISBN:
(数字)9781627058179
Real-time or applied digital signalprocessing courses are offered as follow-ups to conventional or theory-oriented digital signalprocessing courses in many engineering programs for the purpose of teaching students the technical know-how for putting signal processing algorithms or theory into practical use. These courses normally involve access to a teaching laboratory that is equipped with hardware boards, in particular DSP boards, together with their supporting software. A number of textbooks have been written discussing how to achieve real-time implementation on these hardware boards. This book discusses how smartphones can be used as hardware boards for real-time implementation of signal processing algorithms as an alternative to the hardware boards that are currently being used in signalprocessing teaching laboratories. The fact that mobile devices, in particular smartphones, have now become powerful processing platforms has led to the development of this book, thus enabling students to use their own smartphones to run signal processing algorithms in real-time considering that these days nearly all students possess smartphones. Changing the hardware platforms that are currently used in applied or real-time signalprocessing courses to smartphones creates a truly mobile laboratory experience or environment for students. In addition, it relieves the cost burden associated with using a dedicated signalprocessing board noting that the software development tools for smartphones are free of charge and are well-developed. This book is written in such a way that it can be used as a textbook for applied or real time digital signalprocessing courses offered at many universities. Ten lab experiments that are commonly encountered in such courses are covered in the book. This book is written primarily for those who are already familiar with signalprocessing concepts and are interested in their real-time and practical aspects. Similar to existing real-time courses, know
The joint probabilistic data association filter (JPDAF) algorithm is used for tracking multiple targets in clutter. The set JPDA (SJPDA) is a modified version of JPDA filter which is used for tracking targets when the...
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The joint probabilistic data association filter (JPDAF) algorithm is used for tracking multiple targets in clutter. The set JPDA (SJPDA) is a modified version of JPDA filter which is used for tracking targets when they are closely spaced where the JPDA filter fails to track. In this paper, a new method is introduced to switch between these two algorithms for tracking targets based on the validation gates of the tracks. The JPDAF and Set JPDA algorithms are summarized and switching criterion is explained. Finally, the switching criterion is evaluated on three different tracking scenarios. The results show that the switching technique provides better tracking performance than the JPDA filter.
Wired, Wireless and Optical Access Technologies are all vying for space in providing high bandwidth connectivity to the user at his home or office. While each has its domain of prominent use, in the current and near f...
Wired, Wireless and Optical Access Technologies are all vying for space in providing high bandwidth connectivity to the user at his home or office. While each has its domain of prominent use, in the current and near future, these are all expected to co-operate and co-exist harmoniously. Wired access as a means to provide Gigabit connectivity to the customers at their homes or offices to take full benefit of applications like full triple play, or HDTV through Internet, is promising to play a very important part in this connection. This talk will focus on the recent developments in new DSL technologies like VDSL, and the evolving standards like G. Fast, which are making this possible. It will be brought out how signalprocessing continues to play a major role in state-of-the-art Wired Communication Technologies.
Bayesian network classifiers (BNCs) are probabilistic classifiers showing good performance in many applications. They consist of a directed acyclic graph and a set of conditional probabilities associated with the node...
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