P.A.M.-microwave transmission in coloured gaussian noise environment

1977 ◽  
Vol 47 (4) ◽  
pp. 167 ◽  
Author(s):  
Kamilo Feher ◽  
John Huang
Keyword(s):  
Gaussian Noise ◽  
Noise Environment ◽  
Microwave Transmission

scholarly journals A Novel Adaptive State Detector-Based Post-Filtering Active Control Algorithm for Gaussian Noise Environment with Impulsive Interference

2019 ◽  
Vol 9 (6) ◽  
pp. 1176 ◽  
Author(s):  
Wenzhao Zhu ◽  
Shengguo Shi ◽  
Lei Luo ◽  
Jinwei Sun
Keyword(s):  
Active Control ◽  
Gaussian Noise ◽  
Control Algorithm ◽  
Noise Control ◽  
Impulsive Noise ◽  
Active Noise Control ◽  
Noise Environment ◽  
Fast Tracking ◽  
Impulsive Interference ◽  
State Detector

For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.

Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
Kavitha Lakshmi M. ◽  
Koteswara Rao S. ◽  
Subrahmanyam Kodukula
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Gaussian Noise ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Noise Environment ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

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