Design of low-sidelobe pulse compression waveforms

Griffiths, HD; Vinagre, L.

doi:10.1049/el:19940644

Cited by 43 publications

(25 citation statements)

References 2 publications

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“…32. 6 we can see that there are some parameters that determine the structure of the waveform, such as t 0 , determining the duration of the linear part, and δ, representing the nonlinear part. In the process of the design, what makes the matter most is how to choose the parameters' value.…”

Section: Modified Methods Of Designing Nlfmmentioning

confidence: 99%

An Ultra-Low Sidelobe Suppression Method About NLFM

Liu

Wang

Zou

et al. 2015

Lecture Notes in Electrical Engineering

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Pulse compression technology is very useful in modern radar system, with the reason that it can solve the contradiction between the radar range and range resolution. Now, nonlinear frequency modulation (NLFM) is researched widely, because the pulse compression of the NLFM signal can lower the range sidelobe better than À40 dB without window weighting. But in some applications, the reducing level also cannot meet the requirements. This paper put forward a continuous nonlinear FM waveform designed bring down the level of the range sidelobe, which can suppress the range sidelobe to À86 dB. Therefore, it can be used in many radar systems, especially in some kind of weather radar. Keywords Pulse compression • NLFM • Continuous nonlinear FM waveform design IntroductionIn radar system, in order to increase the operating distance as far as possible, we should transmit wide pulse, but the biggest disadvantage of this is that wide pulse will result in the decline of the range resolution. So in order to compromise this contradiction, pulse compression is brought in.Nowadays, linear frequency modulation (LFM) signal and nonlinear frequency modulation (NLFM) signal are widely used in pulse compression. Although LFM is easy to generate, the output of the pulse compression is unsatisfactory. So in order to decrease the sidelobe of the signal, windowing weighting is essential, but which will result in the loss of signal-to-noise ratio and the main lobe of the signal will also be widen. So researchers want to find other signal to improve this problem. NLFM has the ability that it needn't weighting, and the result of the pulse compression is very good. Because of no window weighting, so it will not result in the

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Section: Modified Methods Of Designing Nlfmmentioning

confidence: 99%

An Ultra-Low Sidelobe Suppression Method About NLFM

Liu

Wang

Zou

et al. 2015

Lecture Notes in Electrical Engineering

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“…In some applications such as target detection, a window function or a mismatched filter is utilized to suppress range sidelobes, resulting in the expansion of the main lobe and the loss of the SNR [3], [6], [7]. These problems are not very serious in those target detection applications.…”

Section: Introductionmentioning

confidence: 98%

“…However, the main drawbacks of pulse compression are a blind zone and range sidelobes. Both of these problems have been widely investigated, and there is extensive literature on these topics [3]- [8].…”

Section: Introductionmentioning

confidence: 99%

A Pulse Compression Waveform for Weather Radars With Solid-State Transmitters

Pang

Hoogeboom

Chevalier

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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Due to the contradiction between the high sensitivity requirement and low transmission power of weather radars with solid-state transmitters, a pulse compression technique is necessary. For the purpose of range sidelobe suppression, methods based on amplitude modulation and a mismatched filter are commonly used for target detection radars, which are not applicable for weather observations because of its drawbacks such as main lobe expansion and power loss. This letter presents a nonlinear frequency modulation pulse compression waveform to achieve a very low range sidelobe level. A mathematical model for waveform design is established, in which the time-frequency relation is expressed as the combination of a linear function and a sine series. Thus, a set of parameters can fully characterize the time-frequency curve. By using a simulated annealing algorithm, the optimal parameter set can be obtained, which shows that a peak sidelobe level under −60 dB is achievable. Finally, this kind of waveform is implemented on hardware, and its performance is verified.Index Terms-Multiobjective optimization, nonlinear frequency modulation (NLFM), pulse compression, range sidelobe level, simulated annealing (SA).

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“…For instance, a synthetic aperture radar (SAR) always contains a high range resolution pulse compression, and a downward-looking rain measuring radar with a range sidelobe level of 55 dB is also required [1], [2]. In a satellite-borne rain radar, very stringent requirements on range sidelobe level of 60 dB are demanded [3], and the air traffic control system requires the sidelobe lower than 55 dB under the mainlobe level [4]. In addition, some researchers have devoted themselves to developing the pulse compression algorithms for this century's advanced weather radar to meet the higher time and space resolution requirements [5]- [9].…”

Section: Introductionmentioning

confidence: 99%

A Neural Fuzzy Network Approach to Radar Pulse Compression

Duh

Juang²,

Lin³

2004

IEEE Geosci. Remote Sensing Lett.

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To make good range resolution and accuracy compatible with a high detection capability while maintaining the low average transmitted power, pulse compression processing giving low-range sidelobes is necessary. The traditional algorithms such as the direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter based on three-element Barker code (B13 code) have been developed. Recently, the neural network algorithms were issued. However, the traditional algorithms cannot achieve the requirements of high signal-to-sidelobe ratio and low integrated sidelobe level (ISL), and the normal neural networks such as the backpropagation (BP) network usually produce the extra problems of low convergence speed and are sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network to deal with pulse compression in a radar system is presented. Two different Barker codes are carried out by a six-layer self-constructing neural fuzzy network (SONFIN). Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has significant advantages in noise rejection performance, range resolution ability, and Doppler tolerance, which are superior to the traditional and BP algorithms

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Design of low-sidelobe pulse compression waveforms

Cited by 43 publications

References 2 publications

An Ultra-Low Sidelobe Suppression Method About NLFM

An Ultra-Low Sidelobe Suppression Method About NLFM

A Pulse Compression Waveform for Weather Radars With Solid-State Transmitters

A Neural Fuzzy Network Approach to Radar Pulse Compression

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