Abstract
This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.
Highlights
In recent years, with the development of computers, modern signal processing, and sensor technology, multisensor fusion technology has become a research focus in the field of information processing, and it is widely used in military and civil fields [1,2,3]
We establish an error risk model based on the Bayes criterion and determine the optimal fusion detection algorithm of the distributed sensor system according to the minimum Bayes risk criterion; that is, the optimal fusion rule and the decision rule are jointly obtained to determine whether the detection signal exists
Combined with the short-term predictability of chaotic time series and the sensitivity to small disturbances, based on the distributed sensor local linear autoregressive (DS-LLAR) algorithm, this paper proposes a distributed sensor fusion method to detect impulse signals under the background of chaotic noise
Summary
With the development of computers, modern signal processing, and sensor technology, multisensor fusion technology has become a research focus in the field of information processing, and it is widely used in military and civil fields [1,2,3]. An optimal sensor decision rule based on a two-sensor distributed detection problem under fixed fusion rules was proposed by Tenney and Sandell in 1981 [5]. In order to improve the accuracy and reliability of signal detection, this paper used a coupled local linear model with distributed sensors to perform fusion detection of pulse signals from the background of chaotic noise. The distributed sensor local linear autoregressive (DS-LLAR) model based on the reconstructed sequence is established to obtain one-step prediction error. We establish an error risk model based on the Bayes criterion and determine the optimal fusion detection algorithm of the distributed sensor system according to the minimum Bayes risk criterion; that is, the optimal fusion rule and the decision rule are jointly obtained to determine whether the detection signal exists.
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