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2022, Volume 31, Issue 6

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Sub-Regional Infrared-Visible Image Fusion Using Multi-Scale Transformation
Yexin Liu, Ben Xu, Mengmeng Zhang, Wei Li, Ran Tao
2022, 31(6): 535-550. doi:10.15918/j.jbit1004-0579.2021.096
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Abstract:
Infrared-visible image fusion plays an important role in multi-source data fusion, which has the advantage of integrating useful information from multi-source sensors. However, there are still challenges in target enhancement and visual improvement. To deal with these problems, a sub-regional infrared-visible image fusion method (SRF) is proposed. First, morphology and threshold segmentation is applied to extract targets interested in infrared images. Second, the infrared background is reconstructed based on extracted targets and the visible image. Finally, target and background regions are fused using a multi-scale transform. Experimental results are obtained using public data for comparison and evaluation, which demonstrate that the proposed SRF has potential benefits over other methods.
Distributed Privacy-Preserving Fusion Estimation Using Homomorphic Encryption
Xinhao Yan, Siqin Zhuo, Yancheng Wu, Bo Chen
2022, 31(6): 551-558. doi:10.15918/j.jbit1004-0579.2022.072
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Abstract:
The privacy-preserving problem for distributed fusion estimation scheme is concerned in this paper. When legitimate user wants to obtain consistent information from multiple sensors, it always employs a fusion center (FC) to gather local data and compute distributed fusion estimates (DFEs). Due to the existence of potential eavesdropper, the data exchanged among sensors, FC and user imperatively require privacy preservation. Hence, we propose a distributed confidentiality fusion structure against eavesdropper by using Paillier homomorphic encryption approach. In this case, FC cannot acquire real values of local state estimates, while it only helps calculate encrypted DFEs. Then, the legitimate user can successfully obtain the true values of DFEs according to the encrypted information and secret keys, which is based on the homomorphism of encryption. Finally, an illustrative example is provided to verify the effectiveness of the proposed methods.
Model Predictive Control Based Defensive Guidance Law in Three-Body Engagement
Jinglei Ren, Zhenya Wang, Feng Fang
2022, 31(6): 559-569. doi:10.15918/j.jbit1004-0579.2021.106
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Abstract:
Model predictive control (MPC) has been widely used in process industry, but its applications to missile guidance law are relatively rare. In this paper, MPC is introduced to design defensive guidance law in a three-body engagement, where a defending missile (i.e., defender) is employed to protect a target aircraft from an attacking missile. Based on nonlinear kinematic equations, an explicit linear discrete-time model is derived as the predictive model. Then the defensive guidance problem is formulated as a quadratic programming problem, and a fast algorithm for the MPC guidance law is developed. The advantages of the MPC guidance law include the applicability to scenarios with unknown guidance strategy of attacking missile, nonlinear kinematics and multiple constraints. Another key feature is that the proposed approach does not require alteration in the target maneuver. Simulation results show that the MPC guidance law works well and can meet real-time requirements.
An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion
Haoyu Yang, Yuanshuo Wang, Dongchen Li, Tiancheng Li
2022, 31(6): 570-583. doi:10.15918/j.jbit1004-0579.2022.060
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Abstract:
In this paper, an effective target locating approach based on the fingerprint fusion positioning (FFP) method is proposed which integrates the time-difference of arrival (TDOA) and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios. The FFP method fuses the pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce auxiliary parameters to estimate the target motion state. Subsequently, we can locate the static pedestrians and track the the moving target. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter, unscented Kalman filter and particle filter (PF). The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our proposed approach.
Adaptive Sampling for Near Space Hypersonic Gliding Target Tracking
Guanhua Ding, Jinping Sun, Ying Chen, Juan Yu
2022, 31(6): 584-594. doi:10.15918/j.jbit1004-0579.2021.103
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Abstract:
For modern phased array radar systems, the adaptive control of the target revisiting time is important for efficient radar resource allocation, especially in maneuvering target tracking applications. This paper presents a novel interactive multiple model (IMM) algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles (NSHGV) with a fast adaptive sampling control logic. The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers, thus achieving a balance between tracking accuracy and resource consumption. Simulation results on typical NSHGV targets show that the proposed algorithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.
Distributed Radar Target Tracking with Low Communication Cost
Rui Zhang, Xinyu Zhang, Shenghua Zhou, Xiaojun Peng
2022, 31(6): 595-604. doi:10.15918/j.jbit1004-0579.2022.129
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Abstract:
In distributed radar, most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy. However, as the filtering covariance matrix indicating positioning accuracy often occupies many bits, the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication channels. This paper studies how to compress data for distributed tracking fusion algorithms. Based on the K-singular value decomposition (K-SVD) algorithm, a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix. Then the least square quantization (LSQ) algorithm is used to quantize the data according to the statistical characteristics of the sparse coefficients. Quantized results are then coded with an arithmetic coding method which can further compress data. Numerical results indicate that this tracking data compression algorithm drops the communication bandwidth to 4% at the cost of a 16% root mean squared error (RMSE) loss.
Power Plant Indicator Light Detection System Based on Improved YOLOv5
Yunzuo Zhang, Kaina Guo
2022, 31(6): 605-612. doi:10.15918/j.jbit1004-0579.2022.079
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Abstract:
Electricity plays a vital role in daily life and economic development. The status of the indicator lights of the power plant needs to be checked regularly to ensure the normal supply of electricity. Aiming at the problem of a large amount of data and different sizes of indicator light detection, we propose an improved You Only Look Once vision 5 (YOLOv5) power plant indicator light detection algorithm. The algorithm improves the feature extraction ability based on YOLOv5s. First, our algorithm enhances the ability of the network to perceive small objects by combining attention modules for multi-scale feature extraction. Second, we adjust the loss function to ensure the stability of the object frame during the regression process and improve the convergence accuracy. Finally, transfer learning is used to augment the dataset to improve the robustness of the algorithm. The experimental results show that the average accuracy of the proposed squeeze-and-excitation YOLOv5s (SE-YOLOv5s) algorithm is increased by 4.39% to 95.31% compared with the YOLOv5s algorithm. The proposed algorithm can better meet the engineering needs of power plant indicator light detection.
Airborne GNSS-Receiver Threat Detection in No-Fading Environments
Omid Sharifi-Tehrani
2022, 31(6): 613-620. doi:10.15918/j.jbit1004-0579.2022.005
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Abstract:
Jamming and spoofing detection of global navigation satellite systems (GNSS) is of great importance. Civil and military aerial platforms use GNSS as main navigation systems and these systems are main target of threat attacks. In this paper a simple method based on different empirical probability density functions of successive received signal powers and goodness of fit technique is proposed for airborne platforms such as unmanned aerial vehicle (UAV), in no fading environment. The two different paths between UAV-satellite and UAV-threat, experience different empirical probability density functions which can be used to distinguish between authentic and threat signals. Simulation results including detection and false alarm probabilities show good performance of proposed method as well as low computational burden.

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