VHF/UHF radar. Part 1: Characteristics

Kuschel, Heiner

doi:10.1049/ecej:20020203

Cited by 47 publications

(16 citation statements)

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“…To present the performance of image reconstruction from profile functions, we show two examples of PEC (Perfectly Electric Conducting) objects in freespace, taken from [17] (1) and (3). Note that we choose to center the target and its resulting profile functions at the origin of the Cartesian coordinate system (x, y, z).…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Low frequency radars using UHF (Ultra High Frequency, 300 MHz-3 GHz) and VHF (Very High Frequency, 30 MHz-300 MHz) as well as HF (High Frequency, 1 MHz-30 MHz) correspond to the Rayleigh region and the resonance region for object dimensions respectively small and of the same order, compared to electromagnetic wavelengths [2][3][4]. Although low frequency bands cannot provide high resolution, they can give useful information about the target's size and volume which contributes greatly to obtain target's global shape.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm for Profile Function Calculation of 3d Objects: Application for Radar Target Identification in Low Frequency

Wen

Beaucoudrey

Chauveau³

et al. 2013

PIER B

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Abstract-Ramp response technique in low frequency can be used for generating 3-dimensional images of radar targets (even stealthy or buried targets) so as to identify them. This technique uses the target profile function, which is defined as its transverse crosssectional area versus distance along the observing direction. For mutually orthogonal observing views, reconstructed 3D images are quite accurate. However, in practice, due to the bias introduced from the response in shadow region and from limited non-orthogonal observing directions, reconstructions become distorted. To evaluate the quality of the reconstruction and to further identify objects from their reconstruction, we need to calculate profile functions of 3D reconstructed objects in arbitrary directions. Therefore, in this paper, we propose an algorithm meeting this needs.

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Section: Image Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithm for Profile Function Calculation of 3d Objects: Application for Radar Target Identification in Low Frequency

Wen

Beaucoudrey

Chauveau³

et al. 2013

PIER B

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“…It is the differences though, what makes passive radar attractive; i) as the illuminators are not part of the radar system, its presence is virtually undetectable; ii) illuminators of opportunity are often radio and TV stations, broadcasting in the VHF/UHF frequency bands otherwise not available to radar applications. The benefits of VHF/UHF radar are discussed in [1], [2] Challenges connected to implementing a passive radar system are mostly due to using broadcast signals not under control for illumination. The transmitted signals are not known a priori, so a regular matched filter based receiver cannot be implemented easily.…”

Section: A Passive Radar: Motivation and Challengesmentioning

confidence: 99%

Compressed sensing for OFDM/MIMO radar

Berger

Zhou

Willett

et al. 2008

2008 42nd Asilomar Conference on Signals, Systems and Computers

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In passive radar, two main challenges are: mitigating the direct blast, since the illuminators broadcast continuously, and achieving a large enough integration gain to detect targets. While the first has to be solved in part in the analog part of the processing chain, due to the huge difference of signal strength between the direct blast and weak target reflections, the second is about combining enough signal efficiently, while not sacrificing too much performance. When combining this setup with digital multicarrier waveforms like orthogonal frequency division multiplex (OFDM) in digital audio/video broadcast (DAB/DVB), this problem can be seen to be a version of multiple-input multipleoutput (MIMO) radar. We start with an existing approach, based on efficient fast Fourier transform (FFT) operation to detect target signatures, and show how this approach is related to a standard matched filter approach based on a piece-wise constant approximation of the phase rotation caused by Doppler shift. We then suggest two more applicable algorithms, one based on subspace processing and one based on sparse estimation. We compare these various approaches based on a detailed simulation scenario with two closing targets and experimental data recorded from a DAB network in Germany.

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“…In this paper, we focus on target tracking from preprocessed detections originating from the television or radio broadcasting signals that are modulated according to the digital audio broadcasting (DAB) or digital video broadcasting (DVB) standards [2,3]. The using of the DAB/DVB signals delivers numbers of advantages compared to that of analog signals: such as, the improved detection performance [4], the more effective signal processing process [5], and the more easily estimated multipath.…”

Section: Introductionmentioning

confidence: 99%

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

Shi

Park

Song

2017

EURASIP J. Adv. Signal Process.

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The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with illuminators of opportunity faces two main challenges: the first challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional association ambiguity between the measurements and targets, which introduces a significantly complex three-dimensional (3-D) data association problem among the target-measurement illuminator, this is because all the illuminators transmit the same carrier frequency signals and signals transmitted by different illuminators but reflected via the same target become indistinguishable; the other challenge is that only the bistatic range and range-rate measurements are available while the angle information is unavailable or of very poor quality. In this paper, the authors propose a new target tracking algorithm directly in three-dimensional (3-D) Cartesian coordinates with the capability of track management using the probability of target existence as a track quality measure. The proposed algorithm is termed sequential processing-joint integrated probabilistic data association (SP-JIPDA), which applies the modified sequential processing technique to resolve the additional association ambiguity between measurements and illuminators. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. For reasons of fair comparison, the existing modified joint probabilistic data association (MJPDA) algorithm that addresses the 3-D data association problem via "supertargets" using gate grouping and provides tracks directly in 3-D Cartesian coordinates, is enhanced by incorporating the probability of target existence as an effective track quality measure for track management. Both algorithms deal with nonlinear observations using the extended Kalman filtering. A simulation study is performed to verify the superiority of the proposed SP-JIPDA algorithm over the MJIPDA in this multistatic passive radar system.

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VHF/UHF radar. Part 1: Characteristics

Cited by 47 publications

References 0 publications

Algorithm for Profile Function Calculation of 3d Objects: Application for Radar Target Identification in Low Frequency

Algorithm for Profile Function Calculation of 3d Objects: Application for Radar Target Identification in Low Frequency

Compressed sensing for OFDM/MIMO radar

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

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