IEICE TRANSACTIONS on Fundamentals
Cancellation of Narrowband Interference in GPS Receivers Using NDEKF-Based Recurrent Neural Network Predictors
Summary :
GPS receivers are susceptible to jamming by interference. This paper proposes a recurrent neural network (RNN) predictor for new application in GPS anti-jamming systems. Five types of narrowband jammers, i. e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) training paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.4 pp.954-960
- Publication Date
- 2003/04/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Spread Spectrum Technologies and Applications
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Wei-Lung MAO, Hen-Wai TSAO, Fan-Ren CHANG, "Cancellation of Narrowband Interference in GPS Receivers Using NDEKF-Based Recurrent Neural Network Predictors" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 4, pp. 954-960, April 2003, doi: .
Abstract: GPS receivers are susceptible to jamming by interference. This paper proposes a recurrent neural network (RNN) predictor for new application in GPS anti-jamming systems. Five types of narrowband jammers, i. e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) training paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_4_954/_p
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@ARTICLE{e86-a_4_954,
author={Wei-Lung MAO, Hen-Wai TSAO, Fan-Ren CHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Cancellation of Narrowband Interference in GPS Receivers Using NDEKF-Based Recurrent Neural Network Predictors},
year={2003},
volume={E86-A},
number={4},
pages={954-960},
abstract={GPS receivers are susceptible to jamming by interference. This paper proposes a recurrent neural network (RNN) predictor for new application in GPS anti-jamming systems. Five types of narrowband jammers, i. e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) training paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.},
keywords={},
doi={},
ISSN={},
month={April},}
Copy
TY - JOUR
TI - Cancellation of Narrowband Interference in GPS Receivers Using NDEKF-Based Recurrent Neural Network Predictors
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 954
EP - 960
AU - Wei-Lung MAO
AU - Hen-Wai TSAO
AU - Fan-Ren CHANG
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2003
AB - GPS receivers are susceptible to jamming by interference. This paper proposes a recurrent neural network (RNN) predictor for new application in GPS anti-jamming systems. Five types of narrowband jammers, i. e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) training paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.
ER -
English
