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This paper considers the problem of detection of weak signal detection in noisy chaotic time series using an optimal radial basis function neural network (ORBFNN). Based on chaotic dynamic mechanism, using ORBFNN to establish the forecast model of chaotic time series. When noise exists, to determine the structure of an optimal RBF predictor, we propose a new technique called the cross-validated subspace...
In this paper, a weak signal detection method based on radial basis function (RBF) neural networks is discussed. The principle of weak signal detection with a background noise predictor is that the predictor trained by chaotic time series has a small prediction error, and the prediction error becomes relative large when the input contains a source or target. By exploiting the short-term predictability...
In order to detect a weak periodic signal under the condition of intensive noise, the weak signal including strong background noise and adscititious multiplicative noise were used as input of a monostable stochastic resonance (SR) system. The adscititious noise intensity and the system parameter were adjusted adaptively with genetic algorithm by examining the SR effect on output signal-to-noise ratio...
Signal quantization levels can have significant impact on the performance of a GPS receiver, especially in the present of the interference. In this paper, signal quantization model for estimating interference suppression of the correlator output and evaluating the performance of GPS receiver is presented. With the proposed model, performance of the receiver in acquisition process is demonstrated by...
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