Control Theory & Systems
Maryam Najimi; Akbar Hashemi Borzabadi
Abstract
This paper addresses the challenges of power control, radar assignment, and signal timing to improve the detection and tracking of multiple targets within a mono-static cognitive radar network. A fusion center is utilized to integrate target velocity ...
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This paper addresses the challenges of power control, radar assignment, and signal timing to improve the detection and tracking of multiple targets within a mono-static cognitive radar network. A fusion center is utilized to integrate target velocity data gathered by radars. The primary objective is to minimize the mean square error in target velocity estimation while adhering to constraints related to global detection probability and total radar power consumption for effective target detection and tracking. The optimization problem is formulated and a low-complexity method is proposed using the genetic algorithm (GA). In this approach, the radars and their transmission powers are represented as chromosomes and the network's quality of service (QoS) requirements serve as inputs to the GA. The output of the GA is the mean error square of the target velocity estimation. Once the problem is resolved, the power allocation for each radar assigned to a specific target is determined. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing detection performance and improving tracking accuracy when compared to other benchmark algorithms.
Control Theory & Systems
Alireza Ahangarani Farahani; Abbas Dideban
Abstract
The existing modeling methods using Petri Nets, have been successfully applied to model and analyze dynamic systems. However, these methods are not capable of modeling all dynamic systems such as systems with the current sample time signals, systems including various ...
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The existing modeling methods using Petri Nets, have been successfully applied to model and analyze dynamic systems. However, these methods are not capable of modeling all dynamic systems such as systems with the current sample time signals, systems including various subsystems and multi-mode systems. This paper proposes Hybrid Time Delay Petri Nets (HTDPN) to solve the problem. In this approach, discrete and continuous Petri Nets are combined so that the continuous PNs part and the discrete PNs are responsible for past time samples and current sample time, respectively. To evaluate the performance of the proposed tool, it is employed to model a legless piezoelectric capsubot robot as a multi modes system and a $PID$ controller, in which the gains tuned by the Genetic Algorithm are designed for the resulting model by HTDPN. Results show that the proposed method is faster in terms of mathematical calculations which can reduce the simulation time and complexity of complicated systems. It would be observed that the proposed approach makes the $PID$ controller design simpler as well. In addition, a comparative study of capsubot has been performed. Simulation results show that the presented method is encouraging compared to the predictive control, which is used in the literature.
