Non-linear FM waveform design using Marginal Fisher's Information within the CPM framework

Jakabosky, John; Anglin, Paul M.; Cook, Matthew R.; Blunt, Shannon D.; Stiles, James M.

doi:10.1109/radar.2011.5960590

Cited by 24 publications

(24 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This value is slightly better than the average of the SPEA2 generated codes, but below the average of the NSGA-II generated codes for both values. When the codes are optimized over both ISLR and PSLR, [6] gives a best ISLR value of -6.4 dB and a best PSLR of -30.2 dB. The MOEA generated waveforms have ISLR values equivalent to the best greedy derived phase codes, but have around 8 dB poorer performance in terms of PSLR.…”

Section: Discussionmentioning

confidence: 98%

“…The MOEA generated phase codes also compare favorably to codes generated through other search methods. The study done in [6], used a greedy local search technique to improve known phase codes. The [6] generated codes had a best case performance of -18.7 dB PSLR and -4.9 dB ISLR, when optimized over a single objective.…”

Section: Discussionmentioning

confidence: 99%

“…The study done in [6], used a greedy local search technique to improve known phase codes. The [6] generated codes had a best case performance of -18.7 dB PSLR and -4.9 dB ISLR, when optimized over a single objective. This value is slightly better than the average of the SPEA2 generated codes, but below the average of the NSGA-II generated codes for both values.…”

Section: Discussionmentioning

confidence: 99%

“…The waveforms generated in [6], were optimized strictly in terms of ISLR and PSLR. The MOEA generated waveforms have similar performance in terms of ISLR and PSLR, while having good baseline radar performance; other single objective optimization methods such as the greedy technique have no such performance guarantee.…”

Section: Discussionmentioning

confidence: 99%

“…Some more common codes, such as Barker codes, have been achieved that have desirable properties, however, these codes are limited in length. More recent studies have applied convex optimizers, Particle Swarm Algorithms, and greedy techniques to attempt to create good radar phase codes [4], [5], [6]. The problem at hand is to develop new phase codings for radar waveforms, that are of sufficient length to allow for high P d values and doppler accuracies, with bandwidths sufficient for accurate range determination.…”

Section: Mop Problem Domain Descriptionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Objective Evolutionary Algorithm determined radar phase codes

Stringer

Lamont

Akers

2012

2012 IEEE Radar Conference

View full text Add to dashboard Cite

Abstract-Solving complex real world Multi-Objective Optimization problems is the forte of Multi-Objective Evolutionary Algorithms (MOEA). Such algorithms have been part of many scientific and engineering endeavors. This study applies the NSGA-II and SPEA2 MOEAs to the radar phase coded waveform design problem. The MOEAs are used to generate a series of radar waveform phase codes that have excellent range resolution and Doppler resolution capabilities. The study compares the ability of NSGA-II and SPEA2 to continually evolve (phase code) solutions on the Pareto front for the problem while maintaining a diversity of solutions (phase codes). Results demonstrate that for the radar phase code problem NSGA-II provides a more diverse population of acceptable solutions and therefore a greater number of different viable phase codes when compared to the solutions provided by SPEA2

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Mop Problem Domain Descriptionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Objective Evolutionary Algorithm determined radar phase codes

Stringer

Lamont

Akers

2012

2012 IEEE Radar Conference

View full text Add to dashboard Cite

show abstract

Holistic radar waveform diversity

Blunt

Jakabosky

McCormick

et al. 2018

Academic Press Library in Signal Processing, Volume 7

View full text Add to dashboard Cite

Physical emission of spatially‐modulated radar

Blunt

McCormick

Higgins

et al. 2014

IET Radar, Sonar & Navigation

Self Cite

View full text Add to dashboard Cite

Leveraging the recent development of a physical implementation of arbitrary polyphase codes as spectrally wellcontained waveforms, the notion of spatial modulation is developed whereby a time-varying beampattern is incorporated into the physical emission of an individual pulse. This subset of the broad category of MIMO radar is inspired by the operation of fixational eye movement within the human eye to enhance visual acuity and also subsumes the notion of the frequencydiverse array for application to pulsed radar. From this spatial modulation framework, some specific emission examples are evaluated in terms of resolution and sidelobe levels for the delay and angle domains. The impact of spatial modulation upon spectral content is also considered and possible joint delay-angle emission design criteria are suggested. Simulation results of selected target arrangements demonstrate the promise of enhanced discrimination and the basis for the development of future cognitive radar capabilities that may mimic salient aspects of the visual cortex.

show abstract

Non-linear FM waveform design using Marginal Fisher's Information within the CPM framework

Cited by 24 publications

References 5 publications

Multi-Objective Evolutionary Algorithm determined radar phase codes

Multi-Objective Evolutionary Algorithm determined radar phase codes

Holistic radar waveform diversity

Physical emission of spatially‐modulated radar

Contact Info

Product

Resources

About