Research On Characteristics Analysis And Weak Signal Detection Method Of Small Target In Chaotic Sea Clutter

Sea clutter is influenced by the environmental factors like waves, wind and tides, which is the typical non-stationary signal and has the characteristics of noise. When the radar is observing the sea, the peaks of the sea clutter can lead to serious false alarm. Also, the weak target signal is easily embedded in background sea clutter and noise because of the radar cross section of the small target is extremely small. In addition, the traditional detection methods have the problems such as low precision, poor generalization and worse real-time. Therefore, how to find the small target accurately and reliably is the research emphasis in the filed of radar signal processing.In this paper, the aspects of noise suppression and utilization in the mixed signal of chaos are studied. The article respectively proposes two methods including the adaptive denoising algorithm based on the variance characteristics of EMD components and the adaptive stochastic resonance method for weak signal detection based on Particle Swarm Optimization (PSO). What’s more, the fractal characteristics of sea clutter in Fractional Fourier Transform (FRFT) domain are analysed, and the fractal detection method in single and high dimension are respectively proposed. Further, the fractal clustering method is introduced to screen the sea clutter data, which is used to the selective integrated learning for Support Vector Machine (SVM). In addition, the adaptive fractal clustering method for weak signal detection is proposed. The concrete research content is as follows:This paper studies the variance characteristics of chaotic signal in different chaotic conditions and puts forward an adaptive denoising algorithm based on the Empirical Mode Decomposition (EMD). The arithmetic can adaptively select the IMF layers to be processed according to the relationship between the maximum variance corresponding layers and the total number of decomposition layers of Intrinsic Mode Function (IMF), which also can achieve the intergrated denosing by making use of the lifting wavelet’s advantages in the field of updating and predicting. It carries out the experimental study by the chaotic background noise from Lorenz and Chen System and the measured IPIX radar data. The results show that:under varying degrees of low noise, the proposed method decreases the error of mean square by at least 30% compared with the methods such as traditional wavelet threshold denoising, and the signal to noise ratio has increased about 1.5dB-3.5dB, which means the proposed method can effectively reduce the sea clutter noise to increase the detection effect under the background of chaos.In order to solve the parameter adjustment problems of adaptive stochastic resonance system in the areas of weak signal detection, this article presents a new method to enhance the detection efficiency and availability in the system of two-dimensional Duffing based on Particle Swarm Optimization (PSO). First, the influence of different parameters on the detection performance is analyzed respectively. The correlation between parameter adjustment and stochastic resonance effect is also discussed and converted to the problem of multi-parameter optimization. Second, the experiments including typical chaotic system and sea clutter data are conducted to verify the detection effect Results show the proposed method is highly effective to detect weak periodic signal from chaotic background, and enhance the output SNR greatly.The article studies the fractal characteristics of sea clutter in Fractional FourierTransform (FRFT) domain, which is derived and proved that is influence by order and scale. In addition, the fractional brown motion is used to build the model. According to the FRFT compensation characteristics of radar signal velocity and acceleration, the fractal detection method in single and high dimension is respectively proposed. In the single dimension, the small target detection method is proposed based on adaptive order. The results show the transform order method in sea clutter FRFT domain can detect the small signal in complicated sea condition, which effectively improves the detection threshold and the detection precision is 26.3% higher than the method in time domain. In the high scales, the article presents the small target detection method based on multifractal high scale, and there exists obvious difference between the pure sea clutter and the target unit on negative high scale. Two methods can both solve the disturbance better. Further, the fractal clustering method is introduced to improve the efficiency of support vector machine training and enhance the weak signal detection performance of sea clutter.This paper studies the characteristics and weak signal detection method of small target in the chaotic sea clutter. The denoising and weak signal detection methods are proposed based on the theories of empirical mode decomposition, stochastic resonance and fractal, which can better suppress the interference of sea conditions to small target, and has a certain theoretical significance and practical application value for the recognition of small target and the safety monitoring on sea surface.