The Rao-Blackwellized particle filtering (RBPF) offers a general tracking framework with linear/nonlinear state space models, which outperforms the standard particle filtering in nonlinear and non-Gaussian tracking scenarios. Unfortunately, in conventional radar systems, the observations contain no information about the linear part of target state. In these cases, the RBPF algorithm fails to catch the real trajectories, because we cannot obtain the enough information to correctly update the linear part of target state in the tracking procedure. To overcome such an issue, this paper proposes a Kalman estimation-based BRPF (KE-BRPF) algorithm. In KE-RBPF, the correlation between linear and nonlinear parts of target state is investigated. Benefitting from such investigation, we derive a new set of formulea to present the correlation in terms of means and variances. By utilizing these formulas, our KE-RBPF algorithm correctly tracks the linear part of target state based on the nonlinear one. Finally, the simulation results verify that, our KE-RBPF performs better than other state-of-the-art tracking methods in nonlinear and non-Gaussian radar tracking scenarios, with at least 18% reduction in terms of the means and central tendency of error of tracking root-mean-square-error.

With the advancement of artificial intelligence (AI) technology, edge networks are progressively evolving toward intelligence, and mobile intelligent agents such as uncrewed aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are playing an increasingly vital role in this process. However, existing studies have addressed cooperation among homogeneous agents, such as multiple UAVs, without exploring collaboration between heterogeneous mobile intelligent agents. For this purpose, we propose a vehicle-mounted multi-UAV cooperative service system to collaboratively collect and process multisource heterogeneous Internet of Things (IoTs) tasks under strict latency and resource constraints. To address the complexities of heterogeneous task structures and dynamic collaboration, the article first introduces a multisource heterogeneous task scheduling mechanism, which optimizes task prioritization and resource allocation for efficient processing. In addition, a decentralized reinforcement learning approach based on partial reward decoupling (PRD) heterogeneous agent proximal policy optimization (PRD-HAPPO) is employed to enhance collaboration and trajectory planning between UAVs and vehicles. Simulation results demonstrate that the proposed framework significantly improves task completion efficiency, reduces system latency, and outperforms existing deep reinforcement learning (DRL) algorithms in terms of convergence and scalability.

Object heading detection (OHD) offers potential for research in the control and traffic analysis sectors. Contemporary methodologies in OHD grapple with a set of distinct limitations: a constrained range of detectable object types, a discernible drop in accuracy for oriented bounding box (OBB) predictions influenced by heading estimations, and the inherent limitations associated with the exclusive perspective of bird’s-eye view imagery. This paper introduces HDDet, an advanced method devised for detecting the OBB of objects with heading direction from various viewpoints. It sequentially delineates the Circular Annotation Method (CAM), Multi-dimensional Angle Encoding (MDAE), and the CosWeight strategy. CAM initially couples oriented bounding box data with heading details for accurate target delineation. MDAE follows, optimizing angle encoding to boost the model’s training process. The culmination of this approach is CosWeight, which integrates the Rotated-IoU and heading information into the horizontal box’s loss, thereby enhancing the precision of heading predictions. Rigorous testing across diverse datasets, including the SJTU-L dataset where the heading accuracy increased from 64.41% to 94.82% over OHDet, validates HDDet’s enhanced capabilities, surpassing existing methodologies in both OBB detection and heading accuracy, and marking a significant advancement over the state-of-the-art OHDet. Additionally, the paper presents an engineering vehicle dataset, which is conducive to multi-perspective object heading detection research.

With the development of 6G wireless networks, nonfixed monitoring methods utilizing drone networks as monitoring carriers have been receiving increasing attention. However, due to significant environmental interference affecting the hovering of drones in the air, the video signals often contain substantial random interference, which impacts the performance of current multiobject and multicamera tracking (MOMCT) algorithms. To address this issue, this article proposes a digital-twin-driven multivehicle multidrone monitoring system, in which a simulation scenario consistent with the actual environment is created. Furthermore, a large amount of vehicle monitoring video data from multiple drone perspectives with random interference is generated, thus compensating for the shortage of multicamera vehicle monitoring data, especially data affected by random interference. In addition, an online tracking framework for MOMCT based on the digital-twin system is set up, forming a pipeline that fully utilizes the simulated data generated by the digital-twin system for training and testing. Using this pipeline, we find that a feature detection algorithm combining AttentionNet-CBAM and ResNext101-IBN-A can effectively enhance the ability to identify target features, thereby achieving better multivehicle tracking results. Experimental results on the CityFlow dataset verify that the proposed method has improved the identification $F1$ -score (IDF1) by 1.52% and identification recall (IDR) by 3.58% in comparison with the state-of-the-art online MOMCT methods.

Shadow removal is an important issue in the field of motion object surveillance and automatic control. Although many works are concentrated on this issue, the diverse and similar motion patterns between shadows and objects still severely affect the removal performance. Constrained by the computational efficiency in real-time monitoring, the pixel feature based methods are still the main shadow removal methods in practice. Following this idea, this paper proposes a novel and simple shadow removal method based on a differential correction calculation between the pixel values of Red, Green and Blue channels. Specifically, considering the fact that shadows are formed because of the occlusion of light by objects, all the reflected light will be attenuated. Hence there will be a similar weakening trends in all Red, Green and Blue channels of the shadow areas, but not in the object areas. These trends can be caught by differential correction calculation and distinguish the shadow areas from object areas. Based on this feature, our shadow removal method is designed. Experiment results verify that, compared with other state-of-the-art shadow removal methods, our method improves the average of object and shadow detection accuracies by at least 10% in most of the cases.