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Found 12 papers, 4 papers with code
One-Bit Target Detection in Collocated MIMO Radar with Colored Background Noise
One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume and processing requirements.
Passive detection of a random signal common to multi-sensor reference and surveillance arrays
This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays.
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
The best encoding is the one that is interpretable in nature.
One-Bit Spectrum Sensing for Cognitive Radio
Theoretical analysis is then performed and our results show that the performance loss of the proposed detector is approximately $2$ dB ($\pi/2$) compared to detectors employing $\infty$-bit ADCs when SNR is low.
One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis
To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds.
Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
When either the input or the filter coefficients are known, this is tantamount to assuming that the signals of interest live on a subspace defined by the supporting graph.
One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis
Target detection is an important problem in multiple-input multiple-output (MIMO) radar.
Continual Learning for Infinite Hierarchical Change-Point Detection
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations.
Two-Channel Passive Detection Exploiting Cyclostationarity
This paper addresses a two-channel passive detection problem exploiting cyclostationarity.
Change-Point Detection on Hierarchical Circadian Models
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure.
Model-order selection in statistical shape models
It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM.
Detecting Directionality in Random Fields Using the Monogenic Signal
Classifying a structure as directional or non-directional requires a measure to quantify the degree of directionality and a threshold, which needs to be chosen based on the statistics of the image.
