Habib Rezaei, Mohammad Ali Sebt, Nadali Zarei, Goudarz Saadati Moghadam
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引用次数: 0
Abstract
Typical monopulse radar cannot detect the angle of two closely-spaced targets in one resolution cell. The authors propose a closed-form technique to estimate the angle of two unresolved targets by using a single snapshot of four radar receiver channels. In this work, phase comparison and amplitude comparison problems for monopulse radar are resolved. The monopulse radar angle estimation system has been enhanced using the diagonal difference channel. Using a single pulse, this technique can accurately determine the angle and amplitude of the deceptive decoy and the real target. If both targets have the same direction, the proposed scheme cannot detect them; therefore, to estimate the angle of such targets, we used an additional antenna. The effectiveness of the proposed method is validated by comparing the exact solutions and simulations.
期刊介绍:
IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more.
Topics covered by scope include, but are not limited to:
advances in single and multi-dimensional filter design and implementation
linear and nonlinear, fixed and adaptive digital filters and multirate filter banks
statistical signal processing techniques and analysis
classical, parametric and higher order spectral analysis
signal transformation and compression techniques, including time-frequency analysis
system modelling and adaptive identification techniques
machine learning based approaches to signal processing
Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques
theory and application of blind and semi-blind signal separation techniques
signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals
direction-finding and beamforming techniques for audio and electromagnetic signals
analysis techniques for biomedical signals
baseband signal processing techniques for transmission and reception of communication signals
signal processing techniques for data hiding and audio watermarking
sparse signal processing and compressive sensing
Special Issue Call for Papers:
Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf