Low Sidelobe Range Profile Synthesis for Sonar Imaging Using Stepped-Frequency Pulses

Liu, Xionghou; Sun, Chao; Yang, Yixin; Zhuo, Jie

doi:10.1109/lgrs.2016.2635154

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…In order to guarantee their non-negativities, in this paper, we chose the energy of auto-ambiguity function and the cross-ambiguity and set α = 1 by ignoring the Doppler. Equations (7) and (8) can be expressed as:…”

Section: Convolution Model Of Wideband Cross-ambiguity Functionmentioning

confidence: 99%

“…Therefore, the capability of noise reduction, range sidelobe level and range resolution are the three key issues considered by active radar/sonar. The usual way to solve the problems of high sidelobe level or low resolution is radar/sonar waveform design [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Generally speaking, different waveforms have different resolution performances.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the processing gain to combat noise and the peak-to-sidelobe level are greatly improved. Instead of transmitting multiple waveforms [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], the transmitted signal is a single pulse of LFM in the paper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Guo

Zhao

2020

Sensors

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Matched filtering is widely used in active sonar because of its simplicity and ease of implementation. However, the resolution performance generally depends on the transmitted waveform. Moreover, its detection performance is limited by the high-level sidelobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. This paper proposed a method named iterative deconvolution-time reversal (ID-TR), on which the energy of the cross-ambiguity function is modeled, as a convolution of the energy of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density. Similarly, the generalized target reflectivity density is a convolution of the spread function of channel with the reflectivity density of target as well. The ambiguity caused by the transmitted signal and the spread function of channel are removed by Richardson-Lucy iterative deconvolution and the time reversal processing, respectively. Moreover, this is a special case of the Richardson-Lucy algorithm that the blur function is one-dimensional and time-invariant. Therefore, the iteration deconvolution is actually implemented by the iterative temporal time reversal processing. Due to the iterative time reversal method can focus more and more energy on the strongest target with the iterative number increasing and then the peak-signal power increases, the simulated result shows that the noise reduction can achieve 250 dB in the “ideal” free field environment and 100 dB in a strong multipaths waveguide environment if a 1-ms linear frequency modulation with a 4-kHz frequency bandwidth is transmitted and the number of iteration is 10. Moreover, the range resolution is approximately a delta function. The results of the experiment in a tank show that the noise level is suppressed by more than 70 dB and the reverberation level is suppressed by 3 dB in the case of a single target and the iteration number being 8.

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Section: Convolution Model Of Wideband Cross-ambiguity Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Guo

Zhao

2020

Sensors

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“…Convolutional windowing in time domain used for LFM sidelobe suppression to get lower sidelobe compression output is discussed in [7]. Analysis and demonstration of sidelobe level reduction in sonar imaging using stepped-frequency pulses is introduced in [8].…”

Section: Ahmed Azouz Sameh Ghanem and Abdelrahman Elbardawinymentioning

confidence: 99%

A New Sidelobe Cancellation Method for LFM Radars

Azouz

Ghanem

Elbardawiny

2018

The International Conference on Electrical Engineering

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Compression technique is a vital tool commonly used in radar to increase range resolution and signal to noise ratio. Pulse compression allows achieving the performance of a shorter pulse using a longer pulse and hence gain of a large spectral bandwidth. Unwanted signals from sidelobes returns affect the detection capability of any radar. Different sidelobe reduction/cancellation techniques based on pulse compression for Linear Frequency Modulated (LFM) radars have been deployed and addressed before. In this paper, a new optimum filter for enhancing radar detection capabilities of LFM radars is introduced. The proposed filter response is compared with the windowed classical matched filter response associated with Hamming window function. The filter is implemented using Software Defined Radio (SDR). A practical test has been carried to investigate its performance. Results show superior performance of our proposed matched filter compared to that of classical versions

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“…Convolutional window in time domain is used to LFM sidelobe suppression to get a lower side lobe level as discussed in [8].Xi. Liu, Yixin Yang, and JieZhuo [9] illustrates side lobe reduction levels for sonar imaging using stepped -frequency pulses. In the present paper, a new optimum filter is suggested to completely cancel the levels of side lobes in radar systems compared with that of the matching filter response based on LFM pulse compression.…”

Section: Introductionmentioning

confidence: 99%

New Side Lobe Cancellation Method of Linear Frequency Modulated Radar Signals

Salem*

2020

IJITEE

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Pulse Compression (PC) technique has many advantages in signal processing of radar systems which enhances the radar performance. For a long pulse, the range detection capability can be increased with PC while maintain the advantage of resolution in range for uncompressed pulse. There are many PC techniques such as Binary and Linear Frequency Modulation (LFM) Codes, which can be utilize in radar. The radar detection performance is affected by unwanted signals, which called side lobes that may mask the weaker useful signals, which are present near to strong signals. Pulse compression that uses LFM code is discussed and contrasted with matched filter keep tracked of Hamming windowing filter technique to eliminate the level effect of side lobes. In the present paper, a proposed optimum filter is introduced to enhance both the radar detection capability and resolution in range. The proposed optimum filter representation is evaluated and compared with the classical matched filter response associated with Hamming windowing filter according to the representation of radar detection through Receiver Operating Characteristics (ROC) curves and resolution performance.

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Low Sidelobe Range Profile Synthesis for Sonar Imaging Using Stepped-Frequency Pulses

Cited by 19 publications

References 16 publications

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

A New Sidelobe Cancellation Method for LFM Radars

New Side Lobe Cancellation Method of Linear Frequency Modulated Radar Signals

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