Evidence theoretic classification of ballistic missiles

Bhattacharyya, Arundhati; Saraswat, Vivek; Manimaran, P.; Rao, S. B.

doi:10.1016/j.asoc.2015.08.029

Cited by 7 publications

(5 citation statements)

References 28 publications

(39 reference statements)

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“…Studies have been conducted using the following features: acceleration differences [6], aerodynamic properties [7], acceleration, height and specific energy [8], velocity, energy height and flight angle [9], position, velocity, RCS and acceleration [11], and dynamic RCS sequence [12]. However, several features have similar characteristics at a certain altitude or below during the reentry phase.…”

Section: A Narrowband Featuresmentioning

confidence: 99%

“…Numerous studies focusing on warhead classification have been conducted in the last decade. Classification methods using feature matching [6], [7], the hidden Markov model [8], and the Dempster-Shafer evidence theory (D-S theory) [9], [10] were studied to utilize the target kinematic parameters of the narrowband feature. The support vector machine (SVM) and neural network (NN) were used in [11], [12] to utilize the radar cross section (RCS).…”

Section: Introductionmentioning

confidence: 99%

“…Warhead classification ability was tested by merging the test data collected by two infrared sensors in [4]. The D-S theory was applied to deal with limited a priori data and short observation times for the purpose of ballistic missile classification with radar data in [9]. This theory has also been studied to classify ballistic missiles as a framework to fuse multi-sensor information in [20].…”

Section: Introductionmentioning

confidence: 99%

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Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

et al. 2021

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This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.

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Section: A Narrowband Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

et al. 2021

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“…Suppose the state vector of missile target is x = (x(t), y(t), z(t), v x (t), v y (t), v z (t)) T in Earth centered fixed (ECF) coordinate system. In this case, the state-space model of a midcourse phase missile target has the following form [29 -32] 2 , μ is Earth's gravitational constant and μ = 3.986 006 4×10 5 km 3 /s 2 , J 2 is the second-order gravitational harmonic term of the Earth's gravitational field model and J 2 = −1.082 626 836 × 10 −3 , ω e is the Earth's angular velocity and ω e = 7.292 115 × 10 −5 rad/s, R e represents the Earth's equatorial radius and R e = 6 378.14 km, c e = 3J 2 R 2 e /2.…”

Section: Target Motion Modelmentioning

confidence: 99%

Sensor management of LEO constellation based on covariance control

Qin¹,

Liang²

2019

Journal of Systems Engineering and Electronics

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This paper studies the multi-sensor management problem for low earth orbit (LEO) infrared warning constellation used to track a midcourse missile. A covariance control approach, which selects sensor combinations or subset based on the difference between the desired covariance matrix and the actual covariance of each target, is used for sensor management, including some matrix metrics to measure the differentia between two covariance matrices. Besides, to meet the requirements of the space based warning system, the original covariance control approach is improved. Simulation results demonstrate that the covariance control approach is able to provide a better tracking performance by providing a well-designed desired covariance and balance tracking performance goals with system demands.

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“…Nevertheless, it does not produce desired results when measuring uncertainty of basic probability assignment (BPA) in Dempster-Shafer (D-S) theory that was put forward by Dempster [ 11 ] and then developed by Shafer [ 12 ]. The theory has proved to have significant advantages in representing, processing and fusing uncertain information or data by assigning a probability to the subsets of a set comprising multiple solutions rather than to each of the individual solution [ 13 , 14 ] and has been accepted as de facto standard in many fields [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], such as risk assessment [ 16 , 17 , 18 , 19 ], fault diagnosis [ 20 , 21 , 22 , 23 ], pattern classification [ 24 , 25 , 26 , 27 ], knowledge reasoning [ 28 , 29 ], and sensors’ network analysis [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Deng Entropy and Belief Interval

Zhao

Yang

et al. 2019

Entropy

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It is still an open issue to measure uncertainty of the basic probability assignment function under Dempster-Shafer theory framework, which is the foundation and preliminary work for conflict degree measurement and combination of evidences. This paper proposes an improved belief entropy to measure uncertainty of the basic probability assignment based on Deng entropy and the belief interval, which takes the belief function and the plausibility function as the lower bound and the upper bound, respectively. Specifically, the center and the span of the belief interval are employed to define the total uncertainty degree. It can be proved that the improved belief entropy will be degenerated to Shannon entropy when the the basic probability assignment is Bayesian. The results of numerical examples and a case study show that its efficiency and flexibility are better compared with previous uncertainty measures.

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Evidence theoretic classification of ballistic missiles

Cited by 7 publications

References 28 publications

Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

Sensor management of LEO constellation based on covariance control

An Improved Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Deng Entropy and Belief Interval

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