Graph Neural Networks (GNNs) have been widely used across various fields under the homophily assumption that connected nodes are similar. However, in heterophilic graphs, where connected nodes tend to have dissimilar features, existing GNNs still face some limitations. From the perspective of structure, shallow GNNs could not capture the high-order node information, whereas deep GNNs may suffer from the over-smoothing problem. From the perspective of feature, the useful information of high-order similar nodes is often weakened by low-order dissimilar nodes in the feature update phase. To address the above problems, we propose a Global Structure-aware and Feature-augmented Graph Neural Network (GSF-GNN) to alleviate the limitations from the perspectives of structure and feature. Specifically, from the structure perspective, we design a Structure-based Global Propagation (SGP) module to establish global connections among nodes and adaptively adjust edge weights for message propagation. From the feature perspective, we introduce a Feature-augmented Compensatory Update (FCU) module, which employs a multi-view feature updating mechanism to enhance node features from different perspectives. Our theoretical analysis formally demonstrates the effectiveness of GSF-GNN in heterophilic graphs. Experiments on heterophilic and homophilic benchmark datasets validate the effectiveness of GSF-GNN across various graph structures. Moreover, GSF-GNN achieves stable performance across multiple layers and effectively alleviates the over-smoothing problem. Our codes are available on https://github.com/huijieliu2023/GSF-GNN .

The fusion module of RGB and infrared (IR) remote sensing images is the key of multispectral ship detection. Existing works have shown that the cross-attention-based feature fusion can achieve good performance by extracting the complementary information of RGB and IR modalities. However, the existing commonly used cross-attention mechanisms introduce lots of redundancy parameters and mainly focus on global feature interaction of multispectral images, ignoring local detail information that is also important for small ship detection. In this paper, we propose a novel multispectral ship detection approach named LoGFusion. In LoGFusion, we design the cross stage partial module with partial convolution (CSPMPC) to reduce feature redundancy and utilize the local cross-modal fusion module (LoCFM) and global cross-modal fusion module (GCFM) to capture both local and global cross-modal features. Furthermore, we introduce a Multispectral Small Ship Dataset (MSSD) containing over 5k ship targets for small target detection. Experiments on MSSD validate the effectiveness of our method in terms of small ship detection in multispectral images.

In high-resolution automotive radar tracking systems, vehicle targets are often regarded as extended targets, which means multiple measurements originated from scattering centers of vehicle targets can be detected at each scan and thus the traditional point target tracking schemes are unsuitable. Meanwhile, vehicle maneuvers, e.g., braking and swerving, cause serious degradation of the classical extended target tracking methods. In this paper, a novel method is proposed for maneuvering extended vehicle tracking with automotive radar. The data-region association (DRA) strategy is adopted to handle the vehicle extension effect, which is superior in describing the complex spatial distribution of vehicle target measurements. The interacting multiple model (IMM) method is combined with this DRA strategy to describe the evolution of target motion models. Accordingly, the proposed DRA-IMM method achieves satisfying tracking performance of extended vehicles and also guarantees the robustness in case of maneuvers. Furthermore, in view of the correlation between vehicle extension and its kinematic state, a ray-based strategy is devised to improve the prior distribution of the data-region association of the basic DRA-IMM, and accordingly an enhanced DRA-IMM (EDRA-IMM) method is proposed. Simulation result validates the effectiveness of the proposed DRA-IMM method for maneuvering extended vehicle tracking and the further improvement of the proposed EDRA-IMM method.

This paper deals with the problem of maneuvering target tracking with networked multiple sensors. To avoid linearization of nonlinear function, and obtain more accurate estimate for maneuvering target, a novel distributed maneuvering target tracking method based on interacting multiple model with unscented information consensus protocol is proposed. The pseudo measurement matrix is computed according to unscented transform, based on which the information form of measurements is calculated and local estimate is updated. To unify estimation in different sensors and improve the maneuvering target tracking accuracy throughout the whole network, the weighted information consensus protocol is applied for each model in all sensors. With multiple models interacting, the posterior estimate in each sensor is acquired with weighted combination of the model-conditioned estimates. Experimental results demonstrate that the proposed algorithm outperforms the existing methods in the aspect of tracking accuracy and agreement of estimates in all sensors.

To address radar/ESM track association problem in formation in the presence of systematic biases, an anti-bias track association algorithm based on hierarchical clustering analysis is proposed. The influence of formation and systematic biases on association is analyzed first. In order to eliminate the effect of biases, the relative bearing bias between radar and ESM is estimated by hierarchical clustering for distance vectors in MPC. Finally, anti-bias track association is achieved based on the global optimal assignment. Simulation results indicate the proposed algorithm outperforms the state-of-the-art approaches.

Considering the convergence rate is a very important issue as distributed sensors networks usually consist of low-powered wireless devices and speeding up the consensus convergence rate is also important to reduce the number of messages exchanged among neighbors, a new adaptive method for weight assignment of communication links between sensor nodes is proposed based on the dynamic network topology. Based on the adaptive weight assignment method, an improved Kalman consensus filter (KCF) named IKCF is tailored in this letter for distributed state estimation in sensor networks with cluster structure. Furthermore, the experiments demonstrate the adaptive weight assignment method is effective for distributed state estimation when the sensor network is sparsely deployed. In addition, the simulation results also validate the superior performance of the new algorithm and show that IKCF is an excellent algorithm for multi-clusters sensor networks. And there is no additional communication overhead in IKCF because only some local knowledge is used to autonomously calculate the adaptive consensus rate parameter for each node.