A New Algorithm for Parameter Estimation of LFM Signal
Abstract
When the energy of linear frequency modulation (LFM) signal is constant, its spectrum magnitude square is inversely proportional to frequency modulation slope in the same time duration. Based on this character, every parameter of LFM signal can be estimated. To balance the contradiction between large count amount and high estimation precision, Particle Swarm Optimization (PSO) is introduced to this algorithm. With the fast convergence characteristic of PSO, parameters such as frequency modulation slope and initial frequency are estimated with fewer count amount. Simulation experiments validate that this new algorithm consumes fewer count amount but with higher estimation precision.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Literature [5] combines PSO with FRFT frequency estimation.
“Literature [5] combines the PSO with the FRFT frequency estimation.”
From Fractional Fourier Transform Frequency Estimation Based on Improved Particle Swarm Optimization (2025) · cites this paper as [5]
The abovementioned PSO-FRFT methods still have issues of large computational volume and low estimation accuracy.
“However, the abovementioned methods still have issues of large computational volume and low estimation accuracy.”
From Fractional Fourier Transform Frequency Estimation Based on Improved Particle Swarm Optimization (2025) · cites this paper as [5]
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Cited by 5
- Authors
- Han, N., Chao-xuan, S., & Wang, G.
- Venue
- International Conference on Information Technology and Computer Science, 3rd (ITCS 2011)
- Publisher
- ASME Press
- Published
- 2011
- Pages
- 214-217
- OpenAlex
- W353720648
- Cited by
- 5
- References
- 0
- Citations by year
- 2017: 2 · 2015: 1 · 2012: 1
- FWCI
- 0.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ASME Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Signal Processing Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Electrical Measurement Techniques
- Keywords
- linear frequency modulation signals, initial frequency estimation, particle swarm optimization, computational efficiency
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