An improved fuzzy alpha-beta filter for tracking a highly maneuvering target

Dahmani, Mohammed; Meche, Abdelkrim; Keche, Mokhtar; Abed-Meraim, Karim

doi:10.1016/j.ast.2016.08.029

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…The improved fuzzy alpha-beta filter has been proposed in [29] to track the highly maneuvering targets. A simplified alpha-beta filter based on Gaussian sum has been used by [30].…”

Section: Related Workmentioning

confidence: 99%

An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

et al. 2021

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This paper introduces new learning to the prediction model to enhance the prediction algorithms' performance in dynamic circumstances. We have proposed a novel technique based on the alpha-beta filter and deep extreme learning machine (DELM) algorithm named as learning to alpha-beta filter. The proposed method has two main components, namely the prediction unit and the learning unit. We have used the alpha-beta filter in the prediction unit, and the learning unit uses a DELM. The main problem with the conventional alpha-beta filter is that the values are generally selected via the trial-anderror technique. Once the alpha-beta values are chosen for a specific problem, they remain fixed for the entire data. It has been observed that different alpha-beta values for the same problem give different results. Hence it is essential to tune the alpha-beta values according to their historical behavior for certain values. Therefore, in the proposed method, we have addressed this problem and added the learning module to the conventional -filter to improve the -filter's performance. The DELM algorithm has been used to enhance the conventional alpha-beta filter algorithm's performance in dynamically changing conditions. The model performance has been measured using indoor environmental values of temperature and humidity. The relative improvement in the proposed learning prediction model's accuracy was 7.72% and 16.47% in RMSE and MSE metrics. The results show that the proposed model outperforms in terms of the result as compared to the conventional alpha-beta filter.INDEX TERMS Alpha-beta filter, learning algorithm, prediction algorithm, deep extreme learning machine, energy prediction.

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“…The improved fuzzy alpha-beta filter has been proposed in [29] to track the highly maneuvering targets. A simplified alpha-beta filter based on Gaussian sum has been used by [30].…”

Section: Related Workmentioning

confidence: 99%

An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

et al. 2021

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“…The Alpha Beta filter is derived from the Kalman filter [50]. Alpha Beta filter requires less computation time and memory as compared to the Kalman filter, and the performance is also better as compared to the linear filter [51].…”

Section: B Alpha Beta Filtermentioning

confidence: 99%

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

et al. 2020

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“…Regarding fuzzy logic, Dahmani et al, proposed in [7] a new fuzzy α-β filter for tracking targets with high maneuvering. The coefficients were determined based on tracking index λ (index of maneuverability), having the advantages of being able to detect the starting and ending moment of a maneuver and being easy to implement through the use of the Takagi Sugeno model with few fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Tracking Using Radar Equipment with Fuzzy Logic Algorithm

2020

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Radio-electronic means, including equipment for transmissions, radio-location, broadcasting, and navigation, allow the execution of various research missions and combat forces management. Determining the target coordinates and directing the armament towards them, obtaining and processing data about enemies, ensuring the navigation of ships, planes and outer atmospheric means, transmitting orders, decisions, reports and other necessary information for the armed forces; these are only some of the possibilities of radio-electronic technology. Fuzzy logic allows the linguistic description of the laws of command, operation and control of a system. When working with complex and nonlinear systems, it can often be observed that, as their complexity increases, there is a decrease in the significance of the details in describing the global behavior of the system. Even though such an approach may seem inadequate, it is often superior and less laborious than a rigorous mathematical approach. The main argument in favor of fuzzy set theory is to excel in operating with imprecise, vague notions. This article demonstrates the superiority of a fuzzy tracking system over the standard Kalman filter tracking system under the conditions of uneven accelerations and sudden change of direction of the targets, as well as in the case of failure to observe the target during successive scans. A cascading Kalman filtering algorithm was used to solve the speed ambiguity and to reduce the measurement error in real-time radar processing. The cascade filters are extended Kalman filters with controlled gain using fuzzy logic for tracking targets using radar equipment under difficult tracking conditions.

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An improved fuzzy alpha-beta filter for tracking a highly maneuvering target

Cited by 13 publications

References 12 publications

An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

Aircraft Trajectory Tracking Using Radar Equipment with Fuzzy Logic Algorithm

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