The remaining useful life (RUL) prediction is critical for the safe and reliable operation of lithium-ion battery (LIB) systems, which characterizes the aging status of the battery and provides early warning for battery replacement. Most existing RUL prediction methods rely on empirical aging models, and the role of the battery mechanism is not considered in the subsequent algorithm settings. The accuracy and stability of data-driven algorithms are severely limited by battery aging data. A new electrochemical-model-based particle filter (PF) framework for LIB RUL prediction is proposed in this paper. Parameters of a simplified electrochemical model (SEM) are used as state variables of the PF algorithm and these parameters can be identified by applying specially designed current excitations to the battery. The SEM-based capacity simulation process is taken as the observation equation in the PF algorithm framework. Therefore, the mechanism of the battery is fully considered when making the RUL prediction. The proposed method is validated through cyclic aging experiment of a cylindrical LFP/graphite LIB of 45Ah. The accuracy of the method is compared with a data-driven-based PF framework for RUL prediction and shows better accuracy and stability, which provides a choice for achieving high-quality RUL prediction.

In recent years, ship target detection in synthetic aperture radar (SAR) images has significantly progressed due to the rapid development of deep learning (DL). However, since only the spatial feature information of ship targets is utilized, the current DL-based SAR ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale, rotations, or complex backgrounds. To address these issues, in this paper, a novel deep-learning network for SAR ship rotation detection, called a morphology and topology-based feature alignment network, is proposed which can better exploit the morphological features and inherent topological structure information. This network consists of the following three main steps: First, deformable convolution is introduced to improve the representational ability for irregularly shaped ship targets, and subsequently, a morphology and topology feature pyramid network is developed to extract inherent topological structure information. Second, based on the aforementioned features, a rotation alignment feature head is devised for fine-grained processing as well as aligning and distinguishing the features; to enable regression prediction of rotated bounding boxes; and to adopt a parameter-sharing mechanism to improve detection efficiency. Therefore, utilizing morphological and inherent topological structural information enables a superior detection performance to be achieved. Finally, we evaluate the effectiveness of the proposed method using the rotated ship detection dataset in SAR images (RSDD-SAR). Our method outperforms other DL-based algorithms with fewer parameters. The overall average precision is 90.84% and recall is 92.21%. In inshore and offshore scenarios, our method performs well for the detection of multi-scale and rotation-varying ship targets, with its average precision reaching 66.87% and 95.72%, respectively.

Synthetic aperture radar (SAR) imaging has important application potential in sea environments research, such as submarine detection. It has become one of the most significant research topics in the current SAR imaging field. In order to promote the development and application of SAR imaging technology, a MiniSAR experiment system is designed and developed, which provides a platform for related technology investigation and verification. A flight experiment is then conducted to detect the movement of an unmanned underwater vehicle (UUV) through the wake, which can be captured by SAR. This paper introduces the basic structure and the performance of the experimental system. The key technologies for Doppler frequency estimation and motion compensation, the implementation of the flight experiment, and the image data processing results are given. The imaging performances are evaluated, and the imaging capabilities of the system are verified. The system provides a good experimental verification platform to construct the follow-up SAR imaging dataset of UUV wake and investigate related digital signal processing algorithms.

We propose and experimentally demonstrate a noise mitigation scheme for coherent optical discrete multitone (DMT) signal transmission system. The proposed scheme can mitigate the laser phase noise and additive white Gaussian noise (AWGN) simultaneously when optical DMT signal is coherently detected. In the digital signal process (DSP), the laser phase noise is first estimated and compensated through moving average filter. The AWGN is then reduced by considering the time-domain real-valued or frequency-domain symmetrical conjugation characteristic of DMT signal. Finally, we conduct an experiment to transmit dual-polarization (DP-) DMT signal based on a low complexity coherent detection structure with only two pairs of input and output ports. The experimental results prove that the proposed scheme can enhance the signal-to-noise ratio (SNR) and effectively reduce the effect of laser phase noise even the laser linewidth is as large as 10 MHz. The DP-DMT signal is successfully transmitted over 1920-km standard single mode fiber (SSMF) in the experimental demonstration. The analysis of computational complexity also indicates that the proposed DSP scheme requires less number of complex multiplication operations and has larger laser phase noise tolerance than other DSP schemes.