Threat Assessment Method of Low Altitude Slow Small (LSS) Targets Based on Information Entropy and AHP

Luo, Ruining; Huang, Shucai; Zhao, Yan; Song, Yafei

doi:10.3390/e23101292

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…The entropy weighting method is one of the most important applications of information entropy in the field of social science. Due to its excellent performance, the entropy weighting method is widely used to determine the weights of different attributes in multi-attribute decision-making problems [ 41 , 42 ], and it is also widely used in node importance evaluation in complex networks. Therefore, in this paper, we also use the entropy weighting method to determine the weight of the four local attributes.…”

Section: Preliminariesmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Zhang

Qishan

et al. 2022

Entropy

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Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

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Section: Preliminariesmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Zhang

Qishan

et al. 2022

Entropy

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“…Okello N et al use the Bayesian network to analyze the threat level of an incoming target to the stronghold [31]. Ruining Luo et al use the analytic hierarchy process (AHP) and information entropy to determine the subjective and objective threat factor weights of low altitude, slow speed, and small targets [32]. In the paper, we will use the Dynamic Bayesian Network (DBN) to construct the threat assessment model.…”

Section: Related Workmentioning

confidence: 99%

Section: (B) Evasion Strategymentioning

confidence: 99%

“…Additionally, each criterion has inconsistent effects on subswarm status. Therefore, we use the Analytic Hierarchy Process (AHP) [32] to build a status evaluation hierarchy model, as shown in Figure 4. Then, through a comprehensive assessment of the four criteria, the pairwise comparison matrix 0 X , which depicts the ratio of importance among the criteria, is illustrated in Table 2.…”

Section: (B) Evasion Strategymentioning

confidence: 99%

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A Bilateral Cooperative Strategy for Swarm Escort under the Attack of Aggressive Swarms

Zou

Peng

2022

Electronics

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With the development of swarm intelligence and low-cost unmanned systems, the offence and defense of a swarm have become essential issues in defense and security technologies. A swarm of drones can be used to attack some high-value units (HVUs), such as bases or fuel tanks. Moreover, some moving HVUs such as cargo ships are also greatly threatened when attacked by a swarm of unmanned surface vehicles. A promising approach to protect a HVU from the attack of an aggressive swarm is to use another low-cost swarm. However, escorting a HVU with a swarm is challenging since defenders must respond to attacks and carry out escorts in a noncentralized manner. It is difficult to balance the above tasks well using the unilateral escort strategy adopted by defenders in previous studies. Therefore, this paper proposes a bilateral cooperative strategy for the swarm escort problem under the attack of aggressive swarms. In this bilateral cooperative strategy, the HVU adaptively select different evasion strategies by inferring the threat level according to the spatial distributions of the defenders and attackers. Meanwhile, the defenders of the swarm take a noncentralized escort algorithm by moving around the HVU in a dual-layer formation. Within each layer, the defenders cluster into several uniformly distributed subswarms to counteract the attackers. Numerical simulations are conducted using different aggressive swarm models to demonstrate the effectiveness of the proposed bilateral cooperative strategy.

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“…As a part of the target tracking system, threat assessment is very important, it directly affects the scheduling of sensors, leading to the performance of target tracking decrease. Therefore, it is necessary to study threat assessment in the scheduling of sensors [ 9 , 10 ]. Unfortunately, the above methods are not involved.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight

Zhou

Wei

et al. 2022

Entropy

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In multi-sensor cooperative detection systems, to reduce target threat risk caused by attack tasks and target loss risk induced by uncertain environmental factors, this paper proposes a multi-sensor scheduling method based on joint risk assessment with variable weight. Firstly, considering the target state and prior expert experience of sensor scheduling, this paper gives a new scheme of target threat risk. Then, by combining the given target threat risk and the target loss risk, this paper constructs a joint risk model to meet the diversity of risk assessment. Secondly, a variable-weighted joint risk assessment model is given based on the adaptive weight of target loss risk and target threat risk, and the optimization problem of multi-sensor scheduling is described to minimize the multi-step prediction of the variable-weighted joint risk model. Finally, this paper relaxes above the non-convex optimization problem as a subconvex problem and designs the scheme of multi-sensor scheduling, improving the rapidity and optimization of the sensor scheduling solution. The simulation results show that the proposed method can adaptively schedule sensors and accurately track targets by using minimum sensor resources.

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Threat Assessment Method of Low Altitude Slow Small (LSS) Targets Based on Information Entropy and AHP

Cited by 13 publications

References 15 publications

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy

A Bilateral Cooperative Strategy for Swarm Escort under the Attack of Aggressive Swarms

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight

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