Phase-continuous chirp spread-spectrum signals in a non-gaussian noise environment

In this paper, a spread-spectrum communication system based on a random carrier is proposed which transmits M-ary information. The random signal is considered as a single realization of a random process taken from prescribed symmetric α-stable (SαS) distribution that carries digital M-ary information to be transmitted. Considering the noise model in the channel as additive white Gaussian noise (AWGN), the transmitter sends the information carrying random signal from non-Gaussian density. Alpha-stable distribution is used to encode the M-ary message. Inspired by the chaos shift keying techniques, the proposed method is called M-ary symmetric alpha-stable differential shift keying (M-ary SαS-DSK). The main purpose of preferring non-Gaussian noise instead of conventional pseudo-noise (PN) sequence is to overcome the drawback of self-repeating noise-like sequences which are detectable due to the periodic behavior of the autocorrelation function of PN sequences. Having infinite second order moment in α-stable random carrier offers secrecy of the information due to the non-constant autocorrelation behavior. The bit error rate (BER) performance of the proposed method is illustrated by Monte Carlo simulations with respect to various characteristic exponent values and different data length.

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Low-Power Chirp Spread Spectrum Signals for Wireless Communication Within Nuclear Power Plants

Nuclear Technology ◽

10.13182/nt09-3 ◽

2009 ◽

Vol 166 (2) ◽

pp. 156-169 ◽

Cited By ~ 8

Author(s):

Abdullah Kadri ◽

Raveendra K. Rao ◽

Jin Jiang

Keyword(s):

Wireless Communication ◽

Low Power ◽

Spread Spectrum ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Chirp Spread Spectrum ◽

Spread Spectrum Signals

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Robust DFT based on adaptive censored estimate for FM signal processing in non-Gaussian noise environment

10.1109/isspa.2007.4555465 ◽

2007 ◽

Cited By ~ 1

Author(s):

V.V. Lukin ◽

A.A. Roenko ◽

I. Djurovic ◽

L.J. Stankovic

Keyword(s):

Signal Processing ◽

Gaussian Noise ◽

Noise Environment ◽

Non Gaussian

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Robust adaptive spread-spectrum receiver with neural net preprocessing in non-Gaussian noise

IEEE Transactions on Neural Networks ◽

10.1109/72.925557 ◽

2001 ◽

Vol 12 (3) ◽

pp. 546-558 ◽

Cited By ~ 20

Author(s):

Teong Chee Chuah ◽

B.S. Sharif ◽

O.R. Hinton

Keyword(s):

Gaussian Noise ◽

Spread Spectrum ◽

Neural Net ◽

Robust Adaptive ◽

Non Gaussian

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A new neural network based sequence estimator in non-Gaussian noise environment

Proceedings of International Conference on Neural Networks (ICNN'96) ◽

10.1109/icnn.1996.549136 ◽

2002 ◽

Author(s):

J.F. Weng ◽

S.H. Leung ◽

G.G. Bi

Keyword(s):

Neural Network ◽

Gaussian Noise ◽

Noise Environment ◽

Non Gaussian

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

International Journal of e-Collaboration ◽

10.4018/ijec.2021070101 ◽

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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