Radar Detection Performability under Graceful Degradation

Ridder, Tyler D.; Narayanan, Ram M.

doi:10.23940/ijpe.21.08.p2.666675

Int J Performability Eng

2021

DOI: 10.23940/ijpe.21.08.p2.666675

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Radar Detection Performability under Graceful Degradation

Tyler D. Ridder

Ram M. Narayanan²

Abstract: Operational reliability has been introduced recently as a useful metric to describe the performability of a radar's signal processing system. In this paper, the concept of operational reliability is investigated for a typical radar scenario in which a phased array radar on an airborne platform is tasked with detecting a vehicle on the ground. Various background clutter scenarios are modeled in order to study the effects of clutter type on operational reliability and the associated optimal threshold. The effect… Show more

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Cited by 2 publications

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Reliable Task Planning of Networked Devices as a Multi-Objective Problem Using NSGA-II and Reinforcement Learning

Hou

Zhao

et al. 2022

IEEE Access

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Tracking and detecting multiple objects is difficult for a single radar device, as it may not have the capacities such as anti-interference and anti-stealth. However, if radar devices of diverse capabilities can be combined to realize collaborative networked operation, the reliability and performance of a radar system in a complex environment can be significantly improved. This paper classify the networked radar-based multi-objective task planning as a combinatorial optimization problem with constraints and abstract a distributed multi-agent system (MAS) model from a networked radar system. A node-selection algorithm was designed based on a greedy policy to narrow down solution space for subsequent networked radar task planning, reduce the amount of calculation, and improve the efficiency of the proposed algorithm. Moreover, focusing on NSGA-II, the proposed algorithm was modified using self-adaptive operators and reinforcement learning. A dual-population strategy was introduced to allow exchanges of multiple individuals between populations during migration, and the number of individuals for the exchange was obtained through reinforcement learning. In this paper, five algorithms are compared and analyzed. In addition, statistical analyses are conducted from four perspectives: the average evaluation value of energy consumption and bandwidth in the Pareto front solutions, the time consumption of the algorithm, and the diversity of the population. The results indicate that among those algorithms, the reinforcement-learning-based RNSGA-II algorithm can produce the best outcomes for networked radar task planning.

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Reliable Task Planning of Networked Devices as a Multi-Objective Problem Using NSGA-II and Reinforcement Learning

Hou

Zhao

et al. 2022

IEEE Access

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Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints

Kang

Ren

et al. 2022

Wireless Communications and Mobile Computing

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Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area.

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Radar Detection Performability under Graceful Degradation

Cited by 2 publications

References 16 publications

Reliable Task Planning of Networked Devices as a Multi-Objective Problem Using NSGA-II and Reinforcement Learning

Reliable Task Planning of Networked Devices as a Multi-Objective Problem Using NSGA-II and Reinforcement Learning

Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints

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