For identifying each vessel from ship-radiated noises with only a very limited number of data samples available, an approach based on the contrastive learning was proposed. The input was sample pairs in the training, and the parameters of the models were optimized by maximizing the similarity of sample pairs from the same vessel and minimizing that from different vessels. In practical inference, the method calculated the distance between the features of testing samples and those of registration templates and assigned the testing sample into the closest templates for it to achieve the parameter-free classification. Experimental results on different sea-trial data demonstrated the advantages of the proposed method. On the five-ship identification task based on the open-source data, the proposed method achieved an accuracy of $0.68$ when only five samples per vessel were available, that was significantly higher than conventional solutions with accuracies of $0.26$ and $0.48$. Furthermore, the convergence of the method and the behavior of its performance with increasing data samples available for the training were discussed empirically.

Bearing time record (BTR) is widely used in the field of passive sonar information processing for target detecting and tracking. One of its challenges is to obtain high-resolution beamforming power spectral to facilitate the directions estimating of arrivals, namely DOA estimating. This paper proposes a new algorithm framework Block Matching - Subband Extrema Energy Detection (BM-SEED) to supplement the resolution and enhance the weak targets of BTRs. It extracts the peak features of target trajectories within the conventional beamforming (CBF)-based BTRs by using an improved sub-band peak energy detection (SPED) algorithm, and then enhances the weak targets and suppresses the false-alarm targets through time-spatial autocorrelation analysis. Simulations and sea-trial data evaluations demonstrate that the method of this paper can enhance weak targets, eliminate the interferences of false targets and improve the resolution of BTR targets to 1 degree under −20 dB.

Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.