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Confusion matrix for 1 σ error of 3000 m, 40 m/s, and 5 m/s² in position, velocity, and acceleration
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This study considers the problem of coarse classification of targets using multifunction radar. Several methods are available for classification such as decision trees, Dempster–Shafer, Bayes, neural networks, etc. A different approach to assign the mass functions based on fuzzy logic in the Dempster–Shafer framework is proposed in this study. The...
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Citations
... Due to measurement error, environmental noise, noncooperation of enemy, and other factors, the entries in target database are often missing, inaccurate, and fuzzy. Scholars have carried out relevant researches, such as Bayesian reasoning [1,2], fuzzy sets [3], and evidence theory [4,5]. ...
When the attributes of unknown targets are not just numerical attributes, but hybrid attributes containing linguistic attributes, the existing recognition methods are not effective. In addition, it is more difficult to identify the unknown targets densely distributed in the feature space, especially when there is interval overlap between attribute measurements of different target classes. To address these problems, a novel method based on intuitionistic fuzzy comprehensive evaluation model (IFCEM) is proposed. For numerical attributes, targets in the database are divided into individual classes and overlapping classes, and for linguistic attributes, continuous interval-valued linguistic term set (CIVLTS) is used to describe target characteristic. A cloud model-based method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively. An improved inverse weighted kernel fuzzy c-means (IWK-FCM) algorithm is proposed for solution of attribute weight vector. The possibility matrix is applied to determine the identity and category of query target. Finally, a case study composed of parameter sensitivity analysis, recognition accuracy analysis. and comparison with other methods, is taken to verify the superiority of the proposed method.
... 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). ...
... First, a unified methodology for assigning basic probability (BP) to each proposition has not been fully developed. Many researchers have proposed different methods for assigning BP as follows: non-parametric [24], kernel distribution [25], normal distribution [26], k-mean clustering [27], and fuzzy theory [10], [28]. In the past few years, some studies have used a generalized fuzzy number (GFN) [29], a trapezoidal fuzzy number [30], and a triangular fuzzy number (TFN) [17], [19], [22], [31] methods to improve classification performance. ...
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.
Pre-eclampsia is a complication in pregnancy that is often diagnosed by hypertension and proteinuria in the second tri-semester of pregnancy. Pre-eclampsia could cause suffering for mother and fetus and also increase the risk of mother and child death. The main objective of this research is to develop an expert system to identify the risk of pre-eclampsia in pregnant women. The device was designed with 16 inputs in the form of symptoms and risk factors that influence the disease. This system uses the Dempster Shafer method for classification of 2 classes: there is a risk of pre-eclampsia and no risk of pre-eclampsia. The test consists of 2 aspects, namely the performance test and user satisfaction test. The results of the research for the performance test showed that the system accuracy reached 88.18% with sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value were 92.72%, 83.63%, 85%, and 92%. User satisfaction test results show “good” in all aspects.
