Target Classification and Tracking Based on Aerodynamic Properties and RCS Information Using Rao-Blackwellized Particle Filter

Yoo, Kyungwoo; Chun, Joohwan; Choi, Byung-Gwan

doi:10.1109/piers-fall48861.2019.9021445

Cited by 2 publications

(2 citation 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%

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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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