Sequential-Compressive-Range Azimuth Estimation in Radar Signal Processing

Rahayu, Eny Sukani; Hidayat, Risanuri; Ariananda, Dyonisius Dony; Iswandi, Iswandi

doi:10.1109/icitee53064.2021.9611822

Cited by 1 publication

(1 citation statement)

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“…One of the key study areas is the identification and analysis of nonstationary time series signals, which has been applied in a variety of industries and fields, including streaming media, meteorology, medicine, and business [7][8][9][10][11][12][13][14][15][16][17]. Researchers both domestically and internationally have been using deep learning techniques with significant self-learning capabilities to detect time series signals in recent years, but the signal recognition rate is still suboptimal [18][19][20][21][22][23]. With change point monitoring as a representative application scenario, the extraction of time series signal stationarity indicators and the development of deep learning techniques to enhance signal recognition accuracy represent an unavoidable trend and have significant practical implications.…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning

Li¹,

Cai²,

Liu³

et al. 2022

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Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single Gaussian models, and the change point of the non-stationary time series signal is detected. To further increase the signal's recognition rate, the non-stationary time series signal is preprocessed using the truncated migration algorithm. The main classification task and the auxiliary classification tasks are constructed to identify non-stationary time series signals characterized by huge chaotic classes through multi-task learning. The efficiency of the suggested method and model is validated by experimental data.

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