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The passive radar detection system receives secondary reflection signal by the navigation satellite signal. The secondary reflection signal is too weak to detect the target. The method to improve the signal-to-noise ratio (SNR) of weak targets is proposed based on the accumulation algorithm. First, short-time correlation of the echo signal sequence...
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Citations
... The basic aim in state estimation of the underwater passive target is to accurately approximate the motion variables and target's actual trajectory by obtaining valuable data from noisy measurements of hydrophones [1,2]. Real-time state estimation has many practical applications in both civil and military fields like target detection [3], aircraft surveillance [4], navigation [5], precise guidance [6] and object localization [7]. To achieve accurate state estimation in these real-life problems, nonlinear filtering algorithms are widely applied by the research community in the last two decades because of their efficiency and robustness [8]. ...
In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.
