Showing papers on "Reassignment method published in 2021"

A distributed task reassignment method in dynamic environment for multi-UAV system

Abstract: This paper considers the task reassignment problem for distributed multiple Unmanned Aerial Vehicle (multi-UAV) systems in dynamic environment. For a dynamic reassignment problem in a multi-UAV system, the task information may be subject to different dynamic events, and many existing task allocation algorithms require much computation and communication resource to achieve a feasible solution. Hence, this paper proposes a distributed method to cope with dynamic events that occur online during the execution of original schedules. First, a distributed framework for determining the processing strategy according to the types of dynamic events is introduced. Second, a partial reassignment algorithm (PRA) is proposed to support the framework and an incremental subteam formation mechanism and a partial releasing mechanism are developed to release the computation and communication burden. Furthermore, a modified inclusion phase to maximize assignment (MIP-MA) is also proposed in PRA to maximize the number of task allocations. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution with less data exchanges and runtime.

Micro-Doppler Signal Decomposition Using Second-Order Vertical Synchrosqueezing

Abstract: The concept of using second-order vertical synchrosqueezing (VSS) in the time-frequency (TF) domain for the decomposition of the micro-Doppler signal is described in this paper. In general, the technique aims at sharpening the energy distribution on the TF plane, but contrary to the known reassignment method, allows one to retain phase information and reconstruct the signal or a distinguished component, which favors analysis of multi-component waveforms such as micro-Doppler signatures. Theoretical considerations are briefly described and then an implementation of the technique and its application in order to analyze a real-life micro-Doppler signal is presented. Numerical validation shows that the second-order VSS and an algorithm that serves to extract signal modes can be efficiently used in signal decomposition and target description.

Passive Forward Scattering Signal Extraction Using Second-Order Vertical Synchrosqueezing

Abstract: This paper presents preliminary results of the passive forward scattering radar (FSR) signal extraction in a DVB-T-based passive coherent location (PCL) system using the second-order vertical synchrosqueezing in the time-frequency (TF) domain. The method consists of the energy relocation on the TF plane but, contrary to the reassignment method, allows one to preserve the phase content and retrieve the signal or a particular component (mode). This particular concept is described in this paper. The echo from a cooperative target in a specific geometry such as forward scattering is distinguished, which may be useful in the estimation of its parameters, matched filter designing, and the assessment of the kinematic properties of the target. The research is based on real-life signal processing and the results are illustrated, which confirms the usability of the proposed method in PCL systems.

Tensor analysis of electroencephalogram signal for localization of event-related potentials

Abstract: Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brains physiological, mental and functional abnormalities. EEG is known to be a high-dimensional signal in which processing of information by the brain is reected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations into functions with known spatio-temporal-spectral properties or at least easier to characterize. Multi-channel EEG recordings naturally include multiple modes. Matrix analysis, via stacking or concatenating other modes with the retained two modes, has been extensively used to represent and analyze the EEG data. On the other hand, Multi-way (tensor) analysis techniques keep the structure of the data, and by analyzing more dimensions simultaneously, summarize the data into more interpretable components. This work presents a generalized multi-way array analysis methodology in pattern classification systems as related to source separation and discriminant feature selection in EEG signal processing problems. Analysis of ERPs, as one of the main categories of EEG signals, requires systems that can exploit the variation of the signals in different contextual domains in order to reveal the hidden structures in the data. Temporal, spectral, spatial, and subjects/experimental conditions of multi-channel ERP signals are exploited here to generate three-way and four-way ERP tensors. Two key elements of this framework are the Time-Frequency representation (TFR) and CANDECOMP/PARAFAC model order selection techniques we incorporate for analysis. Here, we propose a fully data-driven TFR scheme, via combining the Empirical Mode Decomposition and Reassignment method, which yields a high resolution and cross-term free TFR. Furthermore, we develop a robust and effective model order selection scheme that outperforms conventional techniques in mid and low SNRs (i.e. 0􀀀10 dB) with a better Probability of Detection (PoD) and almost no extra computational overhead after the CANDECOMP/PARAFAC decomposition. ERP tensor can be regarded as a mixture that includes different kinds of brain activity, artifacts, interference, and noise. Using this framework, the desired brain activity could be extracted out from the mixture. The extracted signatures are then translated for different applications in brain-computer interface and cognitive neuroscience.

High Resolution Time-Frequency Generation with Generative Adversarial Networks

Abstract: Signal representation in Time-Frequency (TF) domain is valuable in many applications including radar imaging and inverse synthetic aparture radar. TF representation allows us to identify signal components or features in a mixed time and frequency plane. There are several well-known tools, such as Wigner-Ville Distribution (WVD), Short-Time Fourier Transform (STFT) and various other variants for such a purpose. The main requirement for a TF representation tool is to give a high-resolution view of the signal such that the signal components or features are identifiable. A commonly used method is the reassignment process which reduces the cross-terms by artificially moving smoothed WVD values from their actual location to the center of the gravity for that region. In this article, we propose a novel reassignment method using the Conditional Generative Adversarial Network (CGAN). We train a CGAN to perform the reassignment process. Through examples, it is shown that the method generates high-resolution TF representations which are better than the current reassignment methods.

High-Order Sychrosqueezing Transforms and Its Application for Highly Accurate Animal Voice Analysis

Abstract: Time-frequency analysis represents the signal in the time-frequency domain and is very important tool for signal analysis, including animal voice analysis. In the conventional time-frequency method, we use the short-time Fourier transform (STFT) to analyze signals. In recent years, the reassignment method becomes more popular, which can improve the time and the frequency resolution simultaneously. Afterwards, the sychrosqueezing transform (SST) and the high-order SST were developed. They both shift the energy-band along the frequency axis. Our proposed architecture combines the high-order SST with many modified methods, which can achieve best resolution of time and frequency and reduce the cross terms.