Wideband Extended Range-Doppler Imaging and Waveform Design in the Presence of Clutter and Noise

Yazıcı, Birsen; Xie, Gang

doi:10.1109/tit.2006.881750

Cited by 27 publications

(13 citation statements)

References 31 publications

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“…Last, the process w (j) (t) is a zero-mean additive white Gaussian noise (AWGN) representing the thermal noise component whose statistical properties do not vary between frames. Due to the fine resolution provided by the UWB signals, single scatterers cannot be considered point reflectors as in narrowband SRNs and should be treated as range-spread objects [15]. In this case, reflections are made up by infinitesimal contributes determined by the illuminated objects surface.…”

Section: A System Modelmentioning

confidence: 99%

“…The ability to distinguish between clutter and useful echoes (i.e., echoes that carry positional information about the targets) depends on the specific environment and application. For example, in synthetic-aperture radars the echoes determined by the background are essential to obtain an accurate image of the environment [15], while in surveillance radar they deteriorate the detection capability [16]. In general, the characteristics of the echoes depend on: (i) the environment (e.g., outdoor, sea, or indoor); (ii) SRN operation (e.g., monostatic vs. multistatic); and (iii) the signaling employed (e.g., continuous wave or impulsive).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Residual Clutter Characterization for UWB Sensor Radar Networks

Morselli

Bartoletti

Conti

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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Sensor radar networks (SRNs) employing ultrawideband (UWB) signals are a prominent solution for accurate localization and tracking in indoor environments. However, tracking device-free targets via SRNs is challenging, especially in environments heavily affected by clutter. Clutter characterization is vital to derive performance benchmarks as well as to design inference algorithms for SRNs. Examples of clutter statistical characterization have been provided in the literature for conventional SRNs employing narrowband signals in outdoor scenarios. However, considerably less effort has been devoted for SRNs employing UWB signals in indoor environments. This paper proposes an approach to characterize the clutter-plus-noise component after mitigation filtering in UWB SRNs. In particular, the statistical properties of the residual clutter-plus-noise are derived by applying statistical tests on measurements gathered in an indoor environment via UWB sensor radar networks.

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Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor Residual Clutter Characterization for UWB Sensor Radar Networks

Morselli

Bartoletti

Conti

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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“…March 16, 2018 DRAFT III. PROPOSED METHOD FOR SOLVING (12) It is difficult to exactly solve (12), as R(θ) is in general not Hermitian. One approximate solution is to consider the inner optimization over a finite number of grid points, θ 1 , θ 2 , .…”

Section: A Robust Waveform Design Based On Statistical Performancementioning

confidence: 99%

Wideband waveform design for robust target detection

Panahi

Ström

Viberg

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Future radar systems are expected to use waveforms of a high bandwidth, where the main advantage is an improved range resolution. In this paper, a technique to design robust wideband waveforms for a Multiple-Input-Single-Output system is developed. The context is optimal detection of a single object with partially unknown parameters. The waveforms are robust in the sense that, for a single transmission, detection capability is maintained over an interval of time-delay and time-scaling (Doppler) parameters. A solution framework is derived, approximated, and formulated as an optimization by means of basis expansion. In terms of probabilities of detection and false alarm, numerical evaluation shows the efficiency of the proposed method when compared with a Linear Frequency Modulated signal and a Gaussian pulse.

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“…The characteristic of rapid time variation in underwater environments may not be suited to the use of signals with a long duration. The other way to combat noise is the design of image reconstruction, which perform Wiener filtering [15] or rational orthogonal wavelet analysis filter bank [16] to suppress noise in active radar on the basis of MIMO waveform design. The shallow water environment is a complicated media with the sea surface upward and the seafloor downward [17,18].…”

Section: Introductionmentioning

confidence: 99%

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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Wideband Extended Range-Doppler Imaging and Waveform Design in the Presence of Clutter and Noise

Cited by 27 publications

References 31 publications

Indoor Residual Clutter Characterization for UWB Sensor Radar Networks

Indoor Residual Clutter Characterization for UWB Sensor Radar Networks

Wideband waveform design for robust target detection

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

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