Adaptive MCPC Radar Waveform Design with Improved Wideband Ambiguity Function

Based on the good correlation and randomness of chaos, this paper uses hybrid Bernouli and Logistic chaotic maps to increase the complexity of chaos, at the same time, we do a combination of discrete frequency coded of non full code sets and poly phase coded, and then hybrid modulation waveforms base on hybrid chaos is proposed for MIMO radar. On the basis of the expression of average ambiguity function is derived, the average fuzzy characteristics of signal waveforms and orthogonality are analyzed in theory. Finally, numerical simulation results validate the correctness of the analysis and the reliability of the signal model.

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Unimodular Waveform Design With Desired Ambiguity Function for Cognitive Radar

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.2019.2942411 ◽

2020 ◽

Vol 56 (3) ◽

pp. 2489-2496 ◽

Cited By ~ 1

Author(s):

Hamid Esmaeili-Najafabadi ◽

Henry Leung ◽

Peter W. Moo

Keyword(s):

Waveform Design ◽

Ambiguity Function ◽

Cognitive Radar

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Optimal Waveform Design Using Frequency-Modulated Pulse Trains for Active Sonar

Sensors ◽

10.3390/s19194262 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4262 ◽

Cited By ~ 2

Author(s):

Chengyu Guan ◽

Zemin Zhou ◽

Xinwu Zeng

Keyword(s):

Optimization Model ◽

Pulse Train ◽

Cross Correlation ◽

Waveform Design ◽

Ambiguity Function ◽

Correlation Property ◽

Initial Pulse ◽

Pulse Trains ◽

Active Sonar ◽

Sonar Systems

Frequency-modulated pulse trains can be applied in active sonar systems to improve the performance of conventional transmitted waveforms. Recently, two pulse trains have been widely researched as the transmitted waveforms for active sonars. The LFM-Costas pulse train was formed by modulating the linear frequency-modulated (LFM) waveform via the Costas sequence to remove the Doppler ambiguity of LFM pulses. The generalized sinusoidal frequency-modulated (GSFM) waveform, another frequency-modulated pulse train, achieved an ideal ambiguity function shape with thumbtack mainlobe. In this paper, we focus on constructing an optimization model to optimize the LFM-Costas and GSFM pulse trains with the genetic algorithm. The pulse trains can be improved on properties of both ambiguity function and correlations between sub-pulses. The optimized pulse trains are proven to have better detection performance than those of the initial pulse trains, including the lower sidelobe levels of ambiguity function, as well as lower cross-correlation property. Moreover, it is affirmed that the reverberation suppression performance of pulse trains has also been improved through the optimization model.

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