Target Threat Assessment Research Articles

A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame.

To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to autonomously classify detected targets into predefined categories with equipment information. To simulate different levels of human guidance, we set up two modes for the model: the easier-to-obtain but low-value-type cues simulated by Mode 1 and the labor-intensive but high-value class labels simulated by Mode 2. In addition, to analyze the respective roles of human experience guidance and machine data learning in target classification tasks, the article proposes a machine-based learner (TCARL_M) with zero human participation and a human-based interventionist with full human guidance (TCARL_H). Finally, based on the simulation data from a wargame, we carried out performance evaluation and application analysis for the proposed models in terms of target prediction and target classification, respectively, and the obtained results demonstrate that TCARL_H-M can not only greatly save labor costs, but achieve more competitive classification accuracy compared with our TCARL_M, TCARL_H, a purely supervised model-long short-term memory network (LSTM), a classic active learning algorithm-Query By Committee (QBC), and the common active learning model-uncertainty sampling (Uncertainty).

Target threat assessment model based on PCA-BP neural network

Abstract In this paper, the air threat received in modern naval warfare is taken as the background, and based on the threat assessment index, PCA and BP neural networks are used to establish a target threat assessment model. Through simulation and analysis, the threat values of different incoming targets are derived, and the data results of this model and other models are compared. It is concluded that the results of this model are basically consistent with the original values, and the error is significantly smaller than that of the other models, which realizes the real-time dynamic detection of threatening targets, and provides a strong support for the subsequent combat program.

Aerial Target Threat Assessment Based on Gated Recurrent Unit and Self-Attention Mechanism

Aerial Target Threat Assessment Based on Gated Recurrent Unit and Self-Attention Mechanism

A New Multi-Target Three-Way Threat Assessment Method with Heterogeneous Information and Attribute Relevance

Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment.

Low-Altitude Slow Small Target Threat Assessment Algorithm by Exploiting Sequential Multifeature With Long Short-Term Memory

Low-Altitude Slow Small Target Threat Assessment Algorithm by Exploiting Sequential Multifeature With Long Short-Term Memory

Threat assessment based on single-valued neutrosophic TOPSIS and three-way decision

This paper provides a novel target threat assessment model that utilizes TOPSIS and three-way decision-making under a single-valued neutrosophic environment. The presented model provides theoretical support for combat decision-making in complex battlefield environments with uncertain information. The model employs single-valued neutrosophic sets to handle uncertain data, which enhances the descriptive ability of information. The maximum deviation method is used to calculate attribute weight factors, which highlights the importance of each attribute. The final target threat ranking is obtained based on the relative closeness coefficient of each target. Furthermore, the proposed model constructs a multi-attribute aggregation loss function matrix for each target, sets the risk avoidance coefficient under the knowledge of the battlefield condition, and calculates the decision threshold of each target using three-way decision theory. This method produces the classification of the target choice. The numerical examples and comparison analysis demonstrate that the suggested model can handle ambiguous scenario information effectively and reasonably, transform traditional decision-making ranking results into three-way classification findings, and provide a rationale for choosing an attacking target.

A Target Threat Assessment Method for Application in Air Defense Command and Control Systems

Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems.

Target Threat Assessment in Air Combat with BP Neural Network for UAV

Target threat assessment is the most important element in command control communication and computer intelligence surveillance reconnaissance (C4ISR) system which is the key function in air combat situation analysis and can select the most valuable target to the commander. A threat assessment model of multi-UAV platform cooperative engagement built based on back propagate neural network (BPNN) is present in this paper, which provides a machine learning method for the threat assessment. The BPNN target threat assessment model has six input nodes, 10 hide nodes and 1 output node, which can fit the complicated nonlinear relationship between threat value and target behavior state. Simulation experiment is executed to verify the accuracy of the proposed BPNN target threat assessment method, and the result shows that the method is effective and can be applied to the real air combat.

Threat assessment of aerial targets based on improved GRA-TOPSIS method and three-way decisions.

Target threat assessment is a critical aspect of information warfare and can offer valuable auxiliary support to combat command decision-making. Aiming to address the shortcomings of three decision-making methods in air combat target assessment, such as the inability to effectively handle uncertain situation information and quantitatively rank the decision-making targets according to their importance, a dynamic intuitionistic fuzzy decision model based on the improved GRA-TOPSIS method and three-way decisions is proposed. First, the target attribute weight is obtained by cosine intuitionistic fuzzy entropy algorithm; then, a novel intuitionistic fuzzy distance measure is introduced, and grey incidence analysis and TOPSIS are used to build the conditional probability for three-way decisions that fully utilize the existing information and reflect the consistency of dynamic change trend; finally, the comprehensive loss function matrix is constructed and the threat classification results are obtained using the decision rules. The example analysis shows that the proposed method can not only effectively handle complex battlefield situations and dynamic uncertain information, but it can also classify targets, improving the effectiveness and rationality of decision-making and providing a reference basis for scientific command decision-making.

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

Air strikes are among the main means of attack in modern warfare. To improve air defense capabilities and aid military decision‐making, threat assessment models have been introduced. As the parameters of the kernel extreme learning machine (KELM) model need to be set individually, this study proposes a parameter learning strategy based on a multistrategy improved sparrow search algorithm (MISSA). First, a reasonable threat assessment model was established based on the capability and situation factors of air targets. Second, the sparrow search algorithm was improved in terms of population position initialization and position update strategy, incorporating tent chaos reverse learning, nonlinear inertia weights, a global search strategy, and adaptive t‐distribution. The effectiveness of the MISSA strategy was verified using nine common benchmark functions. The results show that the proposed MISSA finds an effective balance between global and local searches. Moreover, when the MISSA is applied to solve the tuning problem of KELM, the values of mean absolute percentage error, mean square error, root mean square error, and mean absolute error for MISSA–KELM in the air target threat assessment problem are 2.013 × 10−2, 1.282 × 10−4, 1.132 × 10−2, and 8.316 × 10−3, respectively, all of which are higher than that of the other metaheuristic algorithms (e.g., ACWOA‐KELM and HGWO‐KELM). Therefore, the method proposed in this study can be used as a parameter‐tuning tool for KELM, enabling KELM to perform better in practical applications.

A New Model of Interval-Valued Intuitionistic Fuzzy Weighted Operators and Their Application in Dynamic Fusion Target Threat Assessment.

Existing missile defense target threat assessment methods ignore the target timing and battlefield changes, leading to low assessment accuracy. In order to overcome this problem, a dynamic multi-time fusion target threat assessment method is proposed. In this method, a new interval valued intuitionistic fuzzy weighted averaging operator is proposed to effectively aggregate multi-source uncertain information; an interval-valued intuitionistic fuzzy entropy based on a cosine function (IVIFECF) is designed to determine the target attribute weight; an improved interval-valued intuitionistic fuzzy number distance measurement model is constructed to improve the discrimination of assessment results. Specifically, first of all, we define new interval-valued intuitionistic fuzzy operation rules based on algebraic operations. We use these rules to provide a new model of interval-valued intuitionistic fuzzy weighted arithmetic averaging (IVIFWAA) and geometric averaging (IVIFWGA) operators, and prove a number of algebraic properties of these operators. Then, considering the subjective and objective weights of the incoming target, a comprehensive weight model of target attributes based on IVIFECF is proposed, and the Poisson distribution method is used to solve the time series weights to process multi-time situation information. On this basis, the IVIFWAA and IVIFWGA operators are used to aggregate the decision information from multiple times and multiple decision makers. Finally, based on the improved TOPSIS method, the interval-valued intuitionistic fuzzy numbers are ordered, and the weighted multi-time fusion target threat assessment result is obtained. Simulation results of comparison show that the proposed method can effectively improve the reliability and accuracy of target threat assessment in missile defense.

Target threat estimation based on discrete dynamic Bayesian networks with small samples

The accuracy of target threat estimation has a great impact on command decision-making. The Bayesian network, as an effective way to deal with the problem of uncertainty, can be used to track the change of the target threat level. Unfortunately, the traditional discrete dynamic Bayesian network (DDBN) has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing. Considering the finiteness and discreteness of DDBN parameters, a fuzzy k-nearest neighbor (KNN) algorithm based on correlation of feature quantities (CF-FKNN) is proposed for DDBN parameter learning. Firstly, the correlation between feature quantities is calculated, and then the KNN algorithm with fuzzy weight is introduced to fill the missing data. On this basis, a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning. Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing, and improve the effect of DDBN parameter learning in the case of serious sample missing. With the proposed method, the final target threat assessment results are reasonable, which meets the needs of engineering applications.

Research on threat assessment problems of island air defence system based on the leader-follower model

Abstract To study the command and control system in an island air defence system under the background of systemic conflict, this paper proposes a new modelling method based on the opinion dynamics leader-follower model to simulate the target threat assessment process in the command and control activities. Through example simulation, it is obtained that in a centralised command and control system, a command post (CP)-led model can provide the most accurate threat assessment results, but it consumes a longer time. While in a distributed command and control system, a model without a leader can provide a target threat value accurately and quickly. The consistency of simulation results and actual situation indicates that, at present, under a high degree of battlefield information sharing, it is necessary to down-pass the authority of the CP to improve operational efficiency. In summary, the proposed model can comprehensively reflect the process that the island air defence system assesses the threat of an attacking target, and it gives a new idea for investigating the command and control procedure and system of the island air defence system.

Air Attack Target Threat Assessment Based on Combination Weighting

Abstract Threat assessment is an important process of quantifying the threat of enemy attacking targets. It is also one of the main basis for commanders to make control decisions in air defense operations. Target threat assessment needs to obtain a large amount of air attack target information from various reconnaissance equipment and battlefield sensors, fuse these information, and get the ranking of the threat degree of air attack targets to our side. In view of the unbalanced distribution of index weight in threat assessment in air defense operations, a target threat assessment model based on combined weight is proposed in this paper. Firstly, according to the index system of air raid target threat assessment, the subjective and objective weights of the indexes are determined by analytic hierarchy process and critical method respectively, and the combined weights are calculated by multiplication synthesis method; Then the threat ranking of targets is obtained by TOPSIS method; Finally, the model is verified by an example. The simulation results show that the air target threat assessment model is reasonable.

An Extended Fuzzy CPT-TODIM Model Based on Possibility Theory and its Application to Air Target Dynamic Threat Assessment

Dynamic target threat assessment can be regarded as a classical multiple attribute group decision-making (MAGDM) issue. Triangular fuzzy numbers (TFNs) can fully describe imprecise and fuzzy information for target threat assessment. Thus, to consider the changing trend of target detection information in a certain period and the influence of commanders’ psychological factors on target threat assessment results, this study proposes an extended CPT-TODIM method based on possibility theory (PT-CPT-TODIM) to solve target threat assessment. Of note, the CRITIC method is used to determine the attribute weights of the probability mean matrix and the probability standard deviation matrix, which can make the target threat assessment result more rational. A numerical example and comparison analysis demonstrate the efficiency and feasibility of the newly proposed CPT-TODIM method.

Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network

Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.

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

In order to deal with the new threat of low altitude slow small (LSS) targets in air defense operations and provide support for LSS target interception decision, we propose a simple and reliable LSS target threat assessment method. Based on the detection capability of LSS targets and their threat characteristics, this paper proposes a threat evaluation factor and threat degree quantization function in line with the characteristics of LSS targets. LSS targets not only have the same threat characteristics as traditional air targets but also have the unique characteristics of flexible mobility and dynamic mission planning. Therefore, we use analytic hierarchy process (AHP) and information entropy to determine the subjective and objective threat factor weights of LSS targets and use the optimization model to combine them to obtain more reliable evaluation weights. Finally, the effectiveness and credibility of the proposed method are verified by experimental simulation.

Dynamic Air Target Threat Assessment Based on Interval-Valued Intuitionistic Fuzzy Sets, Game Theory, and Evidential Reasoning Methodology

In order to reduce the uncertainty of target threat assessment results and improve exact target assessment in the complicated and changeable air combat environment, a novel method based on the combination of interval-valued intuitionistic fuzzy sets (IVIFSs), game theory, and evidential reasoning methodology is proposed in this paper. First, the imprecise and fuzzy information of battlefield air target is expressed by IVIFS. Second, the optimal index weight is determined by the interval intuitionistic fuzzy entropy and game theory. And the time series weight is calculated by the inverse Poisson distribution method. Then, the target evaluation information at different times is dynamically fused through an evidential reasoning algorithm. Finally, the accuracy function is applied to obtain the threat ranking of all the targets. A case of the threat assessment of air targets is provided to demonstrate the implementation process of the method proposed in this paper. Simulation experiments show that in a rapidly evolving combat environment, this algorithm can effectively reduce the uncertainty of target threat assessment results. It provides us with a useful way for target threat assessment based on interval-valued intuitionistic fuzzy sets, game theory, and evidential reasoning methodology.

A target threat assessment method for low-altitude aircraft

In recent years, with the gradual opening up of chinese low-altitude airspace, and the government active support for the development of the general aviation and drone industries, the traditional physical protection is mainly aimed at ground threats, resulting in the threat and harassment from air targets has become increasingly prominent. In order to effectively and effectively deal with low-altitude small aircraft in a timely and effective manner, the target threat assessment has become an important part of the command and control process. The target threat degree is the threat degree of the aircraft to the air defense early warning and surveillance system, and is an important basis for handling the detection targets in the safety prevention and control work. The purpose of the threat assessment is to conduct a comprehensive evaluation based on the qualitative and quantitative analysis of the target on the basis of the target identification, and rank the threat levels for all the targets to prepare for the next step.

A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment

Target threat assessment aims to rank targets threat based on their attributes and state information, which provide decision support for subsequent military decisions, e.g. weapon-target optimal assignment. Most existing threat assessment methods can only obtain ranking results, decision-makers usually need to subjectively choose priority targets to attack or interfere based on the preset threat level and ordering results, which does not meet the requirements of complex battlefield situation and uncertain information processing. A method is urgently needed, which can objectively produce threat classification results and automatically provide priority targets for combat. Therefore, we propose a novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment. The core parts are the conditional probability of each target is estimated by intuitionistic fuzzy TOPSIS and the decision thresholds of each target are constructed by intuitionistic fuzzy evaluation values. The results of two numerical examples show that the proposed method can effectively deal with dynamic uncertain situation information, turn the traditional ranking results of two-way decisions to the objective classification results of three-way decisions and can flexibly reflect the acquisition of situation information by setting the risk avoidance coefficient.