Helicopter Classification with a High Resolution LFMCW Radar

Muoz-Ferraras, J.; Pérez-Martínez, F.; Burgos-García, M.

doi:10.1109/taes.2009.5310305

Cited by 25 publications

(30 citation statements)

References 10 publications

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“…However, this classification method is not robust because of the significant class overlap between different helicopters [2,7]. To show this class overlap, several cases are shown in Table 2.…”

Section: Helicopter Classification Problemmentioning

confidence: 99%

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Two helicopter classification methods with a high pulse repetition frequency radar

Zuo

Zhang

et al. 2013

IET Radar, Sonar & Navigation

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In this study, two helicopter classification methods with a high pulse repetition frequency radar are proposed. By analysing the returned signal from a main rotor, the authors find that the essential difference between rotors appears when none of blades is orthogonal to the incident waves. Based on this difference, the authors propose two methods to classify the helicopter. The first one is based on the matched filters (MFs), including the time MF and the time-frequency MF. The second one derives from time-frequency masks. Experimental results demonstrate that both methods have the ability to classify helicopters with the same micro-Doppler parameters at a low signal-to-noise ratio. And they are robust against errors of the estimated parameters and the gesture of the helicopter.

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Section: Helicopter Classification Problemmentioning

confidence: 99%

“…Three parameters of main rotor, the blade number N, the length of blades L, and the rotation rate, are usually considered in helicopter classification [2][3][4][5][6][7]. The standard helicopter classification method is based on the L/N-quotient [2].…”

Section: Introductionmentioning

confidence: 99%

Two helicopter classification methods with a high pulse repetition frequency radar

Zuo

Zhang

et al. 2013

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

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“…Chirp signals are widely used in radar [1], sonar [2], aerospace [3], ultrasound [4], communication [5] and many other areas. A mono-component chirp signal can be described as a signal whose frequency may increase or decrease linearly over time.…”

Section: Introductionmentioning

confidence: 99%

A fast and accurate chirp rate estimation algorithm based on the fractional Fourier transform

Serbes

Aldimashki

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this work, a fast and accurate chirp-rate estimation algorithm is presented. The algorithm is based on the fractional Fourier transform. It is shown that utilization of the golden section search algorithm to find the maximum magnitude of the fractional Fourier transform domains not only accelerates the process, but also increases the accuracy in a noisy environment. Simulation results validate the proposed algorithm and show that the accuracy of parameter estimation nearly achieves the Cramer-Rao lower bound for SNR values as low as −7dB.

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“…Recently, chirp signal is used widely in communication system, biomedical science, acoustics and so on, especially in the new radar systems [1,2]. Because the chirp signal has a low probability of intercept [3], the study on chirp signal detection and parameters estimation has aroused general concern.…”

Section: Introductionmentioning

confidence: 99%

A novel parameter estimation of chirp signal in α-stable noise

Wang

2017

IEICE Electron. Express

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α-stable distribution is a kind of non-Gaussian signal, and it is more likely to exhibit sharp spikes or occasional bursts of outlying observations than one would expect from normally distributed signals. So it is difficult to accurately estimate the parameters of chirp signal in α-stable noise. In addressing this problem, a novel method of accurate parameter estimation of chirp signal is proposed. Firstly, the characteristics of α-stable noise are analysed. Secondly, the chirp signal in α-stable noise is transformed into Gaussian-like distribution. Then, fractional Fourier transform is used to calculate the parameters of the initial frequency and chirp rate. By simulation, the method is verified to be effective and rational. The proposed method can effectively estimate the initial frequency and chirp rate of the chirp signal in α-stable noise.

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Helicopter Classification with a High Resolution LFMCW Radar

Cited by 25 publications

References 10 publications

Two helicopter classification methods with a high pulse repetition frequency radar

Two helicopter classification methods with a high pulse repetition frequency radar

A fast and accurate chirp rate estimation algorithm based on the fractional Fourier transform

A novel parameter estimation of chirp signal in α-stable noise

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