Adaptive detection of small sinusoidal signals in non-Gaussian noise using an RBF neural network

1995 ◽  
Vol 6 (1) ◽  
pp. 214-219 ◽  
Author(s):  
D.M. Hummels ◽  
W. Ahmed ◽  
M.T. Musavi
Keyword(s):  
Neural Network ◽  
Gaussian Noise ◽  
Rbf Neural Network ◽  
Adaptive Detection ◽  
Sinusoidal Signals ◽  
Non Gaussian

Multi-sensor fusion for underwater robot self-localization using PC/BC-DIM neural network

Sensor Review ◽  
2021 ◽  
Vol 41 (5) ◽  
pp. 449-457
Author(s):  
Umair Ali ◽  
Wasif Muhammad ◽  
Muhammad Jehanzed Irshad ◽  
Sajjad Manzoor
Keyword(s):  
Neural Network ◽  
Sensor Fusion ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Sensory Information ◽  
Underwater Robot ◽  
Content Type ◽  
Non Gaussian ◽  
Parametric Filter ◽  
Fusion Approach

Purpose Self-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization. Design/methodology/approach In this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way. Findings Results of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments. Originality/value To the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

Polarimetric adaptive detection in non-Gaussian noise

2003 ◽  
Vol 83 (2) ◽  
pp. 297-306 ◽  
Author(s):  
Antonio De Maio ◽  
Giuseppa Alfano
Keyword(s):  
Gaussian Noise ◽  
Adaptive Detection ◽  
Non Gaussian

Adaptive Detection With Constant False Alarm Ratio in a Non-Gaussian Noise Background

2019 ◽  
Vol 23 (8) ◽  
pp. 1369-1372 ◽  
Author(s):  
Zhen Dai ◽  
Pingbo Wang ◽  
Hongkai Wei ◽  
Yuanchao Xu
Keyword(s):  
False Alarm ◽  
Gaussian Noise ◽  
Adaptive Detection ◽  
Noise Background ◽  
False Alarm Ratio ◽  
Non Gaussian

scholarly journals A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2761
Author(s):  
Dong Chen ◽  
Young Hoon Joo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Gaussian Noise ◽  
Monitoring Network ◽  
Doa Estimation ◽  
Covariance Matrices ◽  
Noise Environment ◽  
Infinity Norm ◽  
The Neural Network ◽  
Non Gaussian

This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular array (UTA) are normalized and then fed into four neural networks for 1D-DOA estimation with identical parameters in parallel; then four 1D-DOA estimations of the UTA can be obtained, and finally, the 3D-DOA estimation could be obtained through post-processing. Secondly, in the presence of non-Gaussian noise, the array output covariance matrices are normalized by the infinity-norm and then processed in Gaussian noise environment; the infinity-norm normalization could effectively suppress impulsive outliers and then provide appropriate input features for the neural network. In addition, the outputs of the neural network are controlled by a signal monitoring network to avoid misjudgments. Comprehensive simulations demonstrate that in Gaussian or non-Gaussian noise environment, the proposed method is superior and effective in computation speed and accuracy in 1D-DOA and 3D-DOA estimations, and the signal monitoring network could also effectively control the neural network outputs. Consequently, we can conclude that CNN has better generalization ability in DOA estimation.

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