Joint waveform optimization and adaptive processing for random phase radar signals

Gorji, Aliakbar A.; Riddolls, R.J.; Ravan, Maryam; Adve, Raviraj

doi:10.1109/taes.2015.130731

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…The above variables are all 3D matrix with size of

K \times M \times N

( K , M and N are the numbers of resolution cells in X , Y and Z directions, respectively). In microwave imaging system, the sampling interval d is large with respect to working wavelength, causing the initial phase

ϕ_{r}

to randomly vary in different resolution cells [22]. Due to the existence of

ϕ_{r}

, the frequency spectrum covers the whole frequency band, which indicates that when sparsely sampled, the broad‐band energy will be mixed with side‐lobe noise, and the direction of maximum variance will not completely cover the desired signal.…”

Section: Signal Processing Algorithmmentioning

confidence: 99%

Sparse sampling‐based microwave 3D imaging using interferometry and frequency‐domain principal component analysis

2017

IET Radar Sonar & Navi

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Microwave radar 3D imaging with high resolution generally requires a great number of samples. The authors aim at accurate reconstruction of microwave radar images while significantly reducing the required number of samples. A novel algorithm is proposed which realises sparse sampling with nearly 50% data reduction and high-quality restoration, based on interferometry and principal component analysis (PCA) in frequency domain. Interferometric processing is utilised to concentrate the frequency spectrum into low-frequency stage, thereby reaching an effective sparse representation of radar image. Furthermore, PCA is introduced to reform radar image according to its principal characteristics in frequency spectrum, without side-lobe artefacts and receiver noise. Experimental data in anechoic chamber demonstrates the great potential of the proposed approach.

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“…The above variables are all 3D matrix with size of

K \times M \times N

ϕ_{r}

to randomly vary in different resolution cells [22]. Due to the existence of

ϕ_{r}

Section: Signal Processing Algorithmmentioning

confidence: 99%

Sparse sampling‐based microwave 3D imaging using interferometry and frequency‐domain principal component analysis

2017

IET Radar Sonar & Navi

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“…Pseudo-random codes with some leakage (CDM)demonstrating the problem Pseudo-random phase codes for MIMO radar have been extensively investigated in the literature [4][5][6], but in many cases Doppler is not considered. This causes two problems: the beamforming process is distorted by the slight phase changes from any movement in the scene over the code repetition interval; and for frequency diverse coding schemes Doppler causes cross-correlation between codes and the resulting leakage of signals distorts the range and cross-range response of the radar.…”

Section: Codes Suitable For Mimo Radarmentioning

confidence: 99%

MIMO radar - some practical considerations

Kilpatrick

Longstaff

2015

2015 IEEE Radar Conference (RadarCon)

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This paper describes practical constraints on the use of MIMO radars imposed by the need for separable codes on each transmitter waveform, and where long code repetition intervals can degrade performance. This is especially problematic for long range radars with many transmitter elements which require long code repetition intervals. This causes beamforming problems if the scene is not phase stationary over this interval. Shorter codes do not help, as they introduce cross correlation which also degrades the beamforming process. We give examples of two useful orthogonal coding schemes to show how range and transmitter numbers extend the code duration and how target Doppler affects the beamforming. We describe a processing technique which will compensate for Doppler, but only if it is constant over a suitable sequence of code repetition intervals. A novel technique is then presented which virtually eliminates these problems. This uses a perimeter array with phase scanned transmitter sub arrays and a two part orthogonal MIMO code. We also identify the unique ability MIMO has to focus at all distances simultaneously in the near field.

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Study of range-extended target detection performance based optimized EBSPK signals

Yao

2018

Cluster Comput

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Joint waveform optimization and adaptive processing for random phase radar signals

Cited by 13 publications

References 18 publications

Sparse sampling‐based microwave 3D imaging using interferometry and frequency‐domain principal component analysis

Sparse sampling‐based microwave 3D imaging using interferometry and frequency‐domain principal component analysis

MIMO radar - some practical considerations

Study of range-extended target detection performance based optimized EBSPK signals

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