Radar phase-coded waveform design using MOEAs

Stringer, Jeremy; Lamont, Gary B.; Akers, Geoffrey A.

doi:10.1109/cec.2012.6256554

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…In summary, the new exploration strategy proposed in this section has the following advantages. First, a comparison between Equations ( 23) and (24) indicates that the improved BeTa adds a normal random distribution to the original random exponential, which increases the range of perturbations of the difference vector X 1 -X 2 in Equation ( 22). However, because the individuals X1 and X2 in Equation ( 22) are in a neighborhood and the evolutionary information gap between them is small, the improved BeTa can further expand the population diversity without destroying the evolutionary direction, satisfying the algorithm's requirements for population diversity and convergence speed.…”

Section: New Exploration Phasementioning

confidence: 99%

“…Their experimental results showed that this method is capable of obtaining polyphase codes with lower autocorrelation sidelobe peak and better Doppler tolerance [23]. Stringer et al used three multi-objective evolutionary algorithms (MOEAs) applied to the issue of designing radar phase coding waveforms; their experimental results showed that NSGA-II-generated phase codes exhibit superior autocorrelation characteristics compared to those generated by SPEA2 or MOEA/D [24].…”

Section: Introductionmentioning

confidence: 99%

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A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm

Wang,

Chen

2023

Remote Sensing

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Realization and enhancement of detection techniques for multiple-input–multiple-output (MIMO) radar systems require polyphase code sequences with excellent orthogonality characteristics. Therefore, orthogonal waveform design is the key to realizing MIMO radar. Conventional orthogonal waveform design methods fail to ensure acceptable orthogonal characteristics by individually optimizing the autocorrelation sidelobe peak level and the cross-correlation sidelobe peak level. In this basis, the multi-objective Archimedes optimization algorithm (MOIAOA) is proposed for orthogonal waveform optimization while simultaneously minimizing the total autocorrelation sidelobe peak energy and total cross-correlation peak energy. A novel optimal individual selection method is proposed to select those individuals that best match the weight vectors and lead the evolution of these individuals to their respective neighborhoods. Then, new exploration and development phases are introduced to improve the algorithm’s ability to increase its convergence speed and accuracy. Subsequently, novel incentive functions are formulated based on distinct evolutionary phases, followed by the introduction of a novel environmental selection method aimed at comprehensively enhancing the algorithm’s convergence and distribution. Finally, a weight updating method based on the shape of the frontier surface is proposed to dynamically correct the shape of the overall frontier, further enhancing the overall distribution. The results of experiments on the orthogonal waveform design show that the multi-objective improved Archimedes optimization algorithm (MOIAOA) achieves superior orthogonality, yielding lower total autocorrelation sidelobe peak energy and total cross-correlation peak energy than three established methods.

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Section: New Exploration Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm

Wang,

Chen

2023

Remote Sensing

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“…In this study, to investigate the algorithm performance, we involve ten unconstrained real-valued MOPs (seven problems with two objectives and three problems with three objectives) from the CEC 2009 competition [13], where the true Pareto front is given for each problem. Therefore, it is possible to estimate the closeness of the found solution to the true Pareto front by (4) and, thus, to estimate the algorithm performance based on this metric.…”

Section: Problem Statementmentioning

confidence: 99%

“…In this study, we present real-valued modifications of three widely used multi-objective genetic algorithms (MOGA): the Strength Pareto Evolutionary Algorithm 2 (SPEA2) [1], the Non-Sorting Genetic Algorithm II (NSGA-II) [2], and the Preference-Inspired Co-Evolutionary Algorithm with goal vectors (PICEA-g) [3]. The effectiveness of these algorithms has been shown on benchmark and real problems in many studies [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

On Island Model Performance for Cooperative Real-Valued Multi-objective Genetic Algorithms

Brester

Ryzhikov

Semenkin

et al. 2018

Lecture Notes in Computer Science

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Solving a multi-objective optimization problem results in a Pareto front approximation, and it differs from single-objective optimization, requiring specific search strategies. These strategies, mostly fitness assignment, are designed to find a set of non-dominated solutions, but different approaches use various schemes to achieve this goal. In many cases, cooperative algorithms such as island model-based algorithms outperform each particular algorithm included in this cooperation. However, we should note that there are some control parameters of the islands' interaction and, in this paper, we investigate how they affect the performance of the cooperative algorithm. We consider the influence of a migration set size and its interval, the number of islands and two types of cooperation: homogeneous or heterogeneous. In this study, we use the real-valued evolutionary algorithms SPEA2, NSGA-II, and PICEA-g as islands in the cooperation. The performance of the presented algorithms is compared with the performance of other approaches on a set of benchmark multi-objective optimization problems.

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“…Наибольшее внимание исследователей сосредоточено на построении ансамблей полифазных и комплементарных КП, последовательностей с нулевой зоной корреляции [1]. При этом используются аналитические методы синтеза [2][3][4], а также генетические алгоритмы [5], эволюцион-ные алгоритмы [6] и различные модификации этих вычислительных процедур [7]. В то же время у комплементарных КП есть ряд ограничений, в том числе на количество последовательностей в наборе, доступные длины КП, а также требование линейности усилителей мощности при реализации.…”

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Formation of Minimax Ensembles of Aperiodic Gold Codes

Zubarev

Ponomarenko

Shanin

et al. 2020

Izv. vysš. učebn. zaved. Ross., Radioèlektron.

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Introduction. Signals constructed on the basis of ensembles of code sequences are widely used in digital communication systems. During development of such systems, the most attention is paid to analysis, synthesis and implementation of periodic signal ensembles. Theoretic methods for synthesis of periodic signal ensembles are developed and are in use. Considerably fewer results are received regarding construction of aperiodic signal ensembles with given properties. Theoretical methods for synthesis of such ensembles are practically nonexistent.Aim. To construct aperiodic Gold code ensembles with the best ratios of code length to ensemble volume among the most known binary codes.Materials and methods. Methods of directed search and discrete choice of the best ensemble based on unconditional preference criteria are used.Results. Full and truncated aperiodic Gold code ensembles with given length and ensemble volume were constructed. Parameters and shape of auto- and mutual correlation functions were shown for a number of constructed ensembles. Comparison of the paper results with known results for periodic Gold code ensembles has been conducted regarding growth of minimax correlation function values depending on code length and ensemble volume.Conclusion. The developed algorithms, unlike the known ones, make it possible to form both complete ensembles and ensembles taking into account the limitation of their volume. In addition, the algorithms can be extended to the tasks of forming ensembles from other families, for example, assembled from code sequences belonging to different families.

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Radar phase-coded waveform design using MOEAs

Cited by 6 publications

References 8 publications

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm

On Island Model Performance for Cooperative Real-Valued Multi-objective Genetic Algorithms

Formation of Minimax Ensembles of Aperiodic Gold Codes

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