Evolutionary computation approach to multi mission waveform design

Amuso, Vincent J.; Antonik, Paul; Schneible, Richard A.; Zhane, Y.

doi:10.1109/radar.2002.1174750

Cited by 4 publications

(2 citation statements)

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“…Although many types of radar have the ability to operate in several modes there is no single radar that can simultaneously perform missions as diverse as AMTI and stripmap SAR using the same waveform. A systems analysis presented in an earlier paper demonstrated the difficulty in designing a single radar system that can achieve these missions simultaneously [3]. Each mission can be mathematically reduced to an objective or set of objectives that must be met and subsequently used to measure mission success.…”

Section: Introductionmentioning

confidence: 99%

“…These objectives are functions of radar parameters such as pulse repetition frequency (prf), center frequency, bandwidth, etc., as well as spatial parameters such as antenna beam width, azimuth look angle, elevation look angle, etc. This objective function representation of a radar multi-mission scenario was proposed originally at RADAR 2002 [3] and elaborated on in more detail at the First International Waveform Diversity & Design Conference in 2004 [4]. Some of the details presented at the 2004 Waveform Diversity conference are repeated in this paper to provide clarity of the proposed technique.…”

Section: Introductionmentioning

confidence: 99%

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The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design

Amuso

Enslin

2007

2007 International Waveform Diversity and Design Conference

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This paper furthers the development of the application of Evolutionary Computation, specifically Genetic Algorithms (GAs) to the design of simultaneously transmitted orthogonal waveforms. The goal of the application is to determine a suite of "optimal" waveforms (in the Pareto sense) for a single platform radar system performing multiple radar missions simultaneously. The waveform suite is determined by applying the Strength Pareto Evolutionary Algorithm 2 (SPEA2) developed by Zitzler, Laumanns & Theile [1] to find waveform parameters that successfully realize a set of objectives particular to a variety of radar missions. The objectives to optimize are dictated by the particular missions of interest. The mapping of these objective functions to actual radar performance parameters is used in the SPEA2 algorithm to determine how best to simultaneously perform multiple radar missions such as GMTI, AMTI, SAR etc. using a single radar system in a Pareto optimal sense. Preliminary results are presented for a scaled down multimission multi-objective function scenario.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design

Amuso

Enslin

2007

2007 International Waveform Diversity and Design Conference

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Pareto-optimal radar waveform design

Maio

Piezzo

Farina

et al. 2011

IET Radar Sonar Navig.

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This study deals with the problem of Pareto-optimal waveform design in the presence of coloured Gaussian noise, under a similarity and an energy constraint. At the design stage, the authors, determine the optimal radar code according the following criterion: constrained maximisation of the detection performance and constrained minimisation of the Cramer-Rao lower bound (CRLB) on the Doppler estimation accuracy. This is tantamount to jointly maximising two quadratic forms under two quadratic constraints, so that the problem can be formulated in terms of a non-convex multi-objective optimisation problem. In order to solve it, the authors resort to the scalarisation technique, which reduces the vectorial problem into a scalar one using a Pareto weight defining the relative importance of the two objective functions. At the analysis stage, the authors assess the performance of the proposed waveform design scheme in terms of detection performance, region of achievable Doppler estimation accuracy and ambiguity function. In particular, the authors analyse the role of the Pareto weight in the optimisation process

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An Evolutionary Algorithm Approach to Simultaneous Multi-Mission Radar Waveform Design

Enslin

2011

Principles of Waveform Diversity and Design

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Evolutionary computation approach to multi mission waveform design

Cited by 4 publications

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The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design

The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design

Pareto-optimal radar waveform design

An Evolutionary Algorithm Approach to Simultaneous Multi-Mission Radar Waveform Design

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