Waveform Diversity for Different Multistatic Radar Configurations

Bradaric, I.; Capraro, Gerard T.; Wicks, Michael C.

doi:10.1109/acssc.2007.4487595

Cited by 22 publications

(14 citation statements)

References 7 publications

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“…In [4], Bradaric et al explore choosing waveforms, sensor positions, and weighting of bistatic pairs to shape the ambiguity function of a multistatic constellation. Noncoherent combination of distributed radar data is explored in [5] from a detection point of view.…”

Section: Introductionmentioning

confidence: 99%

Resolution and sidelobe structure analysis for RF tomography

Parker

Moore

Potter

2011

2011 IEEE RadarCon (RADAR)

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Abstract-Radio frequency (RF) tomography utilizes a network of spatially diverse sensors to trade geometric diversity for bandwidth, permitting images to be formed with narrowband waveforms. Such a constellation of sensors produces a sparsely and irregularly spaced set of Fourier space samples, complicating the definition and analysis of resolution for these systems. We present an analysis of resolution for RF tomography based on the Cramér Rao Bound (CRB) for estimation of target position and velocity. This approach allows the resolution for a given sensor configuration to be determined with minimal computational cost, thus providing a useful design tool for sensor placement and frequency selection for RF tomography. We also explore the impact of Fourier space "filling" with bistatic geometries on sidelobe structure of the ambiguity function. Several simulation results are presented to validate the resolution calculations from the CRB and to illustrate the importance of sensor placement for RF tomographic imaging.

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Section: Introductionmentioning

confidence: 99%

Resolution and sidelobe structure analysis for RF tomography

Parker

Moore

Potter

2011

2011 IEEE RadarCon (RADAR)

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“…In recent years the concept of the ambiguity function was extended to the case of multistatic radar systems [1]- [6]. It was demonstrated that with proper analytical foundation and corresponding graphic representation, the multistatic ambiguity function can serve as a guideline for developing radar fusion and waveform selection strategies.…”

Section: Introductionmentioning

confidence: 99%

Sensor placement for improved target resolution in distributed radar systems

Bradaric

Capraro

Wicks

2008

2008 IEEE Radar Conference

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In this work we investigate rules for sensor placement as a way of improving multistatic radar system performances. We follow the approach of shaping the multistatic ambiguity function, but instead of different weighting rules and different waveforms, as studied in recent works, we consider different system geometries. We concentrate our attention on target resolution as a performance measure and present simulation results that illustrate the significance of adequate sensor placement in multistatic radar systems.

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“…Note that again, the sensors are fixed and the authors are only interested in phase synchronization, i.e., there is no interference in their system. In [5], the authors are interested in finding the optimal locations for multiple transmitters and receivers in radar applications. To our knowledge, the work in [5] and this paper are the first to explore the spatial dimension in this context.…”

Section: Introductionmentioning

confidence: 99%

“…In [5], the authors are interested in finding the optimal locations for multiple transmitters and receivers in radar applications. To our knowledge, the work in [5] and this paper are the first to explore the spatial dimension in this context. This paper introduces the notion of using mobile robots to further enhance the discrimination between target and interference.…”

Section: Introductionmentioning

confidence: 99%

Optimal Beamforming with Mobile Robots

Koch

Adve

Francis

2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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Detection using distributed networks have received significant attention in the space-time adaptive processing literature. The paper examines the use of a new dimension -the mobility of robots to improve the output signal-to-interference-plus-noiseratio (SINR) of an array of antennas. It is proposed that by optimizing over the positions of the antennas in the plane, the SINR can be significantly improved, extending the well-known method of beamforming using optimal complex weights. Initial simulations using a simplistic system scenario show that large gains in SINR are indeed possible when combining the optimal weights and local optimal positions of the receiving antennas.

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Waveform Diversity for Different Multistatic Radar Configurations

Cited by 22 publications

References 7 publications

Resolution and sidelobe structure analysis for RF tomography

Resolution and sidelobe structure analysis for RF tomography

Sensor placement for improved target resolution in distributed radar systems

Optimal Beamforming with Mobile Robots

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