Target Parameter Estimation in Reduced Dimension STAP for Airborne Phased Array Radar

Hussain, Arif; Hussain, Ibrar; Mir, Imran; Afzal, Waqas; Anjum, Umar; Channa, Babar Ali

doi:10.1109/inmic50486.2020.9318130

Cited by 27 publications

(6 citation statements)

References 11 publications

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“…Developing such control systems require well-characterized dynamic system models. Similar work [25,26] has been done in the field of ground robotics [8][9][10][11][12][13]27] as well. Wind tunnel testing (commonly used method for determining the parameters of these dynamic systems) is time-consuming and costly.…”

Section: Introductionmentioning

confidence: 72%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.

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Section: Introductionmentioning

confidence: 72%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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“…Various researchers have employed RL for solving various control problems. Similar nature work [29,30] is done in the field of ground robotics [31][32][33] and wheeled inverted pendulum [25] by employing non-linear Sliding Mode Control (SMC) technique with Adaptive Dynamic Programming. Based on our review of the related research and cited papers, it has been assessed that in spite of being an active area of research, application of RL for UAV application is still in its infancy.…”

Section: Relevant Studiesmentioning

confidence: 97%

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

et al. 2022

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In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.

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“…The main challenge in this field is to coordinate the actions of multiple robots so that they can work together effectively and efficiently [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. In order to solve this problem, researchers often use optimization algorithms, which are mathematical methods that find the best solution to a given problem based on specific objectives and constraints [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Using a group of autonomous mobile robots can have a number of benefits, including increased effectiveness, dependability, and robustness when conducting tasks such as exploration, surveillance, and inspection to acquire information [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

et al. 2023

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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

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Target Parameter Estimation in Reduced Dimension STAP for Airborne Phased Array Radar

Cited by 27 publications

References 11 publications

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

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