A Network Model for Detecting Marine Floating Weak Targets Based on Multimodal Data Fusion of Radar Echoes

Due to the interaction between floating weak targets and sea clutter in complex marine environments, it is necessary to distinguish targets and sea clutter from different dimensions by designing universal deep learning models. Therefore, in this paper, we introduce the concept of multimodal data fusion from the field of artificial intelligence (AI) to the marine target detection task. Using deep learning methods, a target detection network model based on the multimodal data fusion of radar echoes is proposed. In the paper, according to the characteristics of different modalities data, the temporal LeNet (T-LeNet) network module and time-frequency feature extraction network module are constructed to extract the time domain features, frequency domain features, and time-frequency features from radar sea surface echo signals. To avoid the impact of redundant features between different modalities data on detection performance, a Self-Attention mechanism is introduced to fuse and optimize the features of different dimensions. The experimental results based on the publicly available IPIX radar and CSIR datasets show that the multimodal data fusion of radar echoes can effectively improve the detection performance of marine floating weak targets. The proposed model has a target detection probability of 0.97 when the false alarm probability is 10-3 under the lower signal-to-clutter ratio (SCR) sea state. Compared with the feature-based detector and the detection model based on single-modality data, the new model proposed by us has stronger detection performance and universality under various marine detection environments. Moreover, the transfer learning method is used to train the new model in this paper, which effectively reduces the model training time. This provides the possibility of applying deep learning methods to real-time target detection at sea.

A Sea-Surface-Zoning Method Based on Fractal Characteristics

In this paper, a criterion for sea surface zoning based on the fractal characteristics of disturbances is demonstrated. To improve radar detection performance in heavy-tailed sea clutter, a multitude of sea clutter models and corresponding optimum and suboptimum detectors have been designed in recent years. However, there are cases where these models and detectors become insufficient to describe the coexistence of noise and sea clutter. The commonly used signal-to-clutter ratio (SCR) can hardly serve as an indicator revealing which kind of disturbances dominate in a certain area since it is difficult to decide the level of SCR at which sea clutter or noise exceeds the other. Therefore, it is necessary that a set of rules reflecting essential differences between sea clutter and noise are proposed to tell areas where sea clutter dominates, areas where sea clutter and noise coexist and areas where noise dominates. Analyzing fractal characteristics of disturbances, we consider the Hurst exponent H as a feature distinguishing sea clutter and noise from each other. A modified Sigmoid function is employed to model the variation in H with range bins, and the derivative of the function helps to formulate a set of rules for sea surface zoning.

New approach for modelling ultrasound blood backscatter signal

The ultrasound (US) scattered signal from blood has been treated as a random signal by many investigators. However, the degree of randomness of a medium is a relative term that can change considerably with the resolution of the sensor. In this study, the backscattered signal from blood has been looked at as a chaotic signal. By this treatment, according to Taken's theorem, a single variable (e.g., amplitude of the blood-backscattered signal) can be used to reconstruct the nonlinear dynamics of the blood-scattered signal. Multilayer perceptron neural network architecture, with error back-propagation, has been formulated and used as a basis for building and testing the chaotic model of the backscattered signal. This chaotic model is used successfully as a short-term predictor of the backscattered signal from blood-mimicking fluid (BMF) flowing in a vascular flow phantom under pulsatile flow. This modelling approach can be useful, for example, in detecting blood-borne emboli.