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DOA Estimation in the Presence of Doppler Shifts Using quantum-inspired Swarm Intelligence algorithms

SN Computer Science 2024年第4期5卷 398页

作者： P, Geetha Nanda, Satyasai Jagannath Yadav, Rajendra Prasad Department of Electronics and Communication Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur 302017 India

Direction of arrival (DOA) estimation is an important problem of wireless sensor network (WSN) where the objective is to calculate the angle of incidence of the source signal at the sensor array. The estimation performance degrades due to the presence of Doppler shifts, which are caused by the mobility of sources. To determine the effective solution, here new models based on quantum-inspired swarm intelligence algorithms are proposed for DOA estimation: quantum-inspired sailfish optimization (QSFO), quantum-inspired gray wolf optimization (QGWO), quantum-inspired whale optimization (QWoA), and quantum-inspired particle swarm optimization (QPSO). The beauty of these algorithms is that they provide high-speed convergence on large-scale optimization problems. The quantum entanglement principle is embodied in the swarm intelligence algorithms to improve the exploration capability of the population in the hunting phase. The DOA estimation is carried out using a maximum likelihood estimator (MLE) with quantum-inspired swarm intelligence algorithms in these scenarios: (1) variation of Doppler frequencies, (2) variation of a number of sources, (3) different noise conditions (SNR variation), (4) different noise environments (stationary and non-stationary), and (5) different types of sources (uncorrelated and correlated). Superior DOA estimation results are reported for the proposed algorithm for different Doppler frequencies. The performance of the proposed quantum-inspired algorithms is also compared with two state-of-the-art classical algorithms: MUSIC and ESPIRIT. Proposed QSFO offers better results for 4 CEC, 12 uni-modal, and 12 multi-modal benchmark functions. A practical application of the proposed approach is demonstrated to solve DOA estimation with Doppler shift in automotive radar. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Automotive radar Direction of arrival estimation Doppler shift quantum sailfish optimizer quantum-inspired optimization algorithms

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Scientometric analysis of quantum-inspired metaheuristic algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期57卷 22-22页

作者： Pooja Sood, Sandeep Kumar Natl Inst Technol Kurukshetra Dept Comp Applicat Kurukshetra 136119 Haryana India

quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.

关键词： quantum-inspired optimization algorithms quantum computing quantum-inspired evolutionary algorithms CiteSpace

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