In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.

A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection of FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based methods do not fully exploit the short-term local characteristics and long-term dependencies of power grid data and have poor correlation with past and future time series information, resulting in a lack of credibility in the detection results. In view of this, an FDIA detection model combining a bidirectional temporal convolutional network and bidirectional gated recurrent unit with an attention mechanism (A-BiTG) was proposed. The proposed model utilizes a bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) to consider past and future temporal information in the grid. This enhances the ability of the model to capture long-term dependencies and extract features, while also solving the model’s problem of exploding and vanishing gradients. In addition, an attention mechanism (AM) was added to dynamically assign weights to the extracted feature information and retain the most valuable features to improve the detection accuracy of the model. Finally, the proposed method was compared with existing methods on the IEEE 14-bus and IEEE 118-bus test systems. The results show that the proposed detection model is more robust and superior under different noise environments and FDIA signals with different intensities.

In many cases, target tracking is subjected to a dense, non-uniform, and time-varying clutter background. This will seriously deteriorate the tracking performance under an unknown clutter environment. In this paper, multi-target tracking under the unknown environment is considered. First, the finite mixture distribution (FMD) is used to fit unknown clutter distributions and then Gibbs sampling and Bayesian information criterion (BIC) are adopted to evaluate and estimated clutter parameters. Besides, the unknown detection profile and clutter rate are also considered. All these issues are solved in the random finite set (RFS) framework because it is a principled and top-down approach. Finally, several experiments are provided to verify the proposed algorithm

In multitarget tracking, knowledge of the backgrounds plays a crucial role in the accuracy of the tracker. Clutter and detection probability are the two essential background parameters which are usually assumed to be known constants although they are, in fact, unknown and time varying. Incorrect values of these parameters lead to a degraded or biased performance of the tracking algorithms. This paper proposes a method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection. An effective filter is developed from parallel estimation of these parameters and then feeding them into the state-of-the-art generalized labeled multi-Bernoulli filter. Provided that the fluctuation of these unknown backgrounds is slowly-varying in comparison to the rate of measurement-update data, the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.

Detection of marine oil spills and accurate estimation of the oil spill concentration distribution are essential in an emergency response. This article proposes an iterative adaptation density-based spatial clustering of applications with noise (IA-DBSCAN) algorithm, which aims to estimate the surface characteristics of marine oil spills, including source localization and concentration distribution. This algorithm addresses the issues of traditional density-based spatial clustering of applications with noise (DBSCAN) algorithms, such as the inability to obtain density information and the lack of autonomy in parameter selection. We utilize laser-induced fluorescence (LIF) technology to monitor marine oil spills. According to the Bernoulli model, we convert the collected fluorescence intensity into the density of fluorescent points. The density distribution of these fluorescent points can be used to estimate the characteristics of the oil spill surface. This brings other challenges: clustering under mixed-density conditions and accurately calculating the concentration of spilled oil. To address these challenges, a mean filtering method based on the distance-weighted from oil source points was proposed to smooth the concentration of fluorescent points, which improves the estimation accuracy of the oil spill surface as well as reduces the fluorescence detection errors caused by wave and environmental factors. Finally, tests were conducted in both marine simulation environments and laboratory simulation environments. The results of the tests indicate that the proposed method is capable of accurately estimating the characteristics of the oil spill surface.