Performance analysis of different autoregressive methods for spectrum estimation along with their real time implementations

Chakraborty, Debashis; Sanyal, Sugata

doi:10.1109/icrcicn.2016.7813646

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Notably, the coefficients a k in the MCOV method could be solved from a set of linear equations as in equation [8],…”

Section: Hrrp Based On Ar Algorthmmentioning

confidence: 99%

Detection of Range-Spread Target in Spatially Correlated Weibull Clutter Based on AR Spectral Estimation

Bai

2021

IEICE Trans. Fundamentals

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In high range resolution radar systems, the detection of range-spread target under correlated non-Gaussian clutter faces many problems. In this paper, a novel detector employing an autoregressive (AR) model is proposed to improve the detection performance. The algorithm is elaborately designed and analyzed considering the clutter characteristics. Numerical simulations and measurement data verify the effectiveness and advantages of the proposed detector for the range-spread target in spatially correlated non-Gaussian clutter.

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“…Notably, the coefficients a k in the MCOV method could be solved from a set of linear equations as in equation [8],…”

Section: Hrrp Based On Ar Algorthmmentioning

confidence: 99%

Detection of Range-Spread Target in Spatially Correlated Weibull Clutter Based on AR Spectral Estimation

Bai

2021

IEICE Trans. Fundamentals

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“…The first part contains multiple layers of restricted Boltzmann machines in order to pre train the network [44]. The second part is the feed forward back propagation network, which will further refine the results from restricted Boltzmann machines (RBM) stack [45], [46].…”

Section: Deep Belief Network (Dbn)mentioning

confidence: 99%

Implementation of feature extraction and deep learning-based ensemble classifier for interference mitigation in radar signals

Indira

Rao

2021

IJEECS

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In automotive vehicles, radar is the one of the component for autonomous driving, used for target detection and long-range sensing. Whereas interference exists in signals, noise increases and it effects severely while detecting target objects. For these reasons, various interference mitigation techniques are implemented in this paper. By using these mitigation techniques interference and noise are reduced and original signals are reconstructed. In this paper, we proposed a method to mitigate interference in signal using deep learning. The proposed method provides the best and accurate performance in relate to the various interference conditions and gives better accuracy compared with other existing methods.

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“…In [14], a hidden bivariate Markov chain is applied to model the received signal in a Gaussian channel, and it can predict the state in time domain. With regard to regression analysis, ARMA shows better performance in predicting cyclostationary time series than non-stationary time series, while the ARIMA model has better prediction performance for non-stationary time series [15]. In addition, the above prediction methods are all directed to a narrowband spectrum.…”

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

Wang

Yuan-cheng

et al. 2019

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Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance.

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Performance analysis of different autoregressive methods for spectrum estimation along with their real time implementations

Cited by 7 publications

References 23 publications

Detection of Range-Spread Target in Spatially Correlated Weibull Clutter Based on AR Spectral Estimation

Detection of Range-Spread Target in Spatially Correlated Weibull Clutter Based on AR Spectral Estimation

Implementation of feature extraction and deep learning-based ensemble classifier for interference mitigation in radar signals

Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

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