The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Yonina C Eldar - One of the best experts on this subject based on the ideXlab platform.
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Range-Doppler Decoupling and Interference Mitigation using Cognitive Random Sparse Stepped Frequency Radar
2020 IEEE Radar Conference (RadarConf20), 2020Co-Authors: Kumar Vijay Mishra, Satish Mulleti, Yonina C EldarAbstract:Stepped frequency waveform (SFW) radars are used for synthesizing high range resolution profiles (HRRP). SFW radars suffer from strong range-Doppler coupling and are not robust to coexisting spectral interference. In this paper, we propose a new random, sparse step-frequency radar (RaSSteR) waveform to address these shortcomings. Unlike SFW where the carrier frequency is linearly increased over the available bandwidth, RaSSteR randomizes the frequency sequence to decouple range and Doppler. This new waveform also skips portions of the transmit spectrum without decreasing the range resolution and operates cognitively by focusing all its power in only a few frequencies. We derive theoretical guarantees which demonstrate that, even with few subpulses, RaSSteR has identical Target Recovery performance as the conventional random stepped frequency (RSF) waveform. Numerical experiments show that RaSSteR's Target hit rate has a 30% improvement over the conventional RSF.
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ICASSP - Theoretical Analysis of Multi-Carrier Agile Phased Array Radar
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Tianyao Huang, Yimin Liu, Nir Shlezinger, Yonina C EldarAbstract:Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manner. To enhance the Target Recovery performance of FAR in complex electromagnetic environments, and particularly, its range-Doppler Recovery performance, multi-Carrier AgilE phaSed Array Radar (CAESAR) was proposed. CAESAR extends FAR to multi-carrier waveforms while introducing the notion of spatial agility. In this paper, we theoretically analyze the range-Doppler Recovery capabilities of CAESAR. Particularly, we derive conditions which guarantee accurate reconstruction of these range-Doppler parameters. These conditions indicate that by increasing the number of frequencies transmitted in each pulse, CAESAR improves performance over conventional FAR, especially in complex environments where some radar measurements are severely corrupted by interference.
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TenDSuR: Tensor-Based 4D Sub-Nyquist Radar
IEEE Signal Processing Letters, 2019Co-Authors: Kumar Vijay Mishra, Yimin Liu, Yonina C Eldar, Xiqin WangAbstract:We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the Target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-OMP and tensor completion algorithms for signal Recovery. Our numerical experiments demonstrate joint estimation of all three Target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid Target Recovery with respect to tensor-OMP.
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conditions for Target Recovery in spatial compressive sensing for mimo radar
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Marco Rossi, Alexander M Haimovich, Yonina C EldarAbstract:We study compressive sensing in the spatial domain for Target localization in terms of direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit/receive elements are placed at random. This allows to dramatically reduce the number of elements, while still attaining performance comparable to that of a filled (Nyquist) array. Leveraging properties of a (structured) random measurement matrix, we develop a novel bound on the coherence of the measurement matrix, and we obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish respectively uniform and non-uniform Recovery guarantees for Target localization using spatial compressive sensing. In particular, nonuniform Recovery is guaranteed if the number of degrees of freedom (the product of the number of transmit and receive elements MN) scales with K(log G)2, where K is the number of Targets, and G is proportional to the array aperture and determines the angle resolution. The significance of the logarithmic dependence in G is that the proposed framework enables high resolution with a small number of MIMO radar elements. This is in contrast with a filled virtualMIMO array where the product MN scales linearly with G.
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ICASSP - Conditions for Target Recovery in spatial compressive sensing for MIMO radar
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Marco Rossi, Alexander M Haimovich, Yonina C EldarAbstract:We study compressive sensing in the spatial domain for Target localization in terms of direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit/receive elements are placed at random. This allows to dramatically reduce the number of elements, while still attaining performance comparable to that of a filled (Nyquist) array. Leveraging properties of a (structured) random measurement matrix, we develop a novel bound on the coherence of the measurement matrix, and we obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish respectively uniform and non-uniform Recovery guarantees for Target localization using spatial compressive sensing. In particular, nonuniform Recovery is guaranteed if the number of degrees of freedom (the product of the number of transmit and receive elements MN) scales with K(log G)2, where K is the number of Targets, and G is proportional to the array aperture and determines the angle resolution. The significance of the logarithmic dependence in G is that the proposed framework enables high resolution with a small number of MIMO radar elements. This is in contrast with a filled virtualMIMO array where the product MN scales linearly with G.
Randolph L. Moses - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Sparse Target Recovery performance of multi-frequency chirp waveforms
2011Co-Authors: Emre Ertin, Lee C. Potter, Randolph L. MosesAbstract:Imaging radars employ wideband linear frequency modulation (LFM) waveforms to achieve high resolution while maintaining moderate sampling rates through restricting the Target support to a known range interval and using stretch (deramp) processing. In recent work motivated by compressive sensing principles, multi-frequency extensions of the chirp waveforms were proposed to obtain randomized projections of range profiles. This paper considers the sparse Target Recovery problem with chirp transmit waveforms and their multi-frequency extensions. We derive the sensing matrix for multi-frequency chirp waveforms and study its coherence properties. We show that multi-frequency chirp waveforms result in sensing matrices with lower coherence between the columns resulting in improved Target estimation performance compared to the standard LFM waveform.
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Sparse Target Recovery performance of multi-frequency chirp waveforms
2011 19th European Signal Processing Conference, 2011Co-Authors: Emre Ertin, Lee C. Potter, Randolph L. MosesAbstract:Imaging radars employ wideband linear frequency modulation (LFM) waveforms to achieve high resolution while maintaining moderate sampling rates through restricting the Target support to a known range interval and using stretch (deramp) processing. In recent work motivated by compressive sensing principles, multi-frequency extensions of the chirp waveforms were proposed to obtain randomized projections of range profiles. This paper considers the sparse Target Recovery problem with chirp transmit waveforms and their multi-frequency extensions. We derive the sensing matrix for multi-frequency chirp waveforms and study its coherence properties. We show that multi-frequency chirp waveforms result in sensing matrices with lower coherence between the columns resulting in improved Target estimation performance compared to the standard LFM waveform.
Yimin Liu - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Theoretical Analysis of Multi-Carrier Agile Phased Array Radar
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Tianyao Huang, Yimin Liu, Nir Shlezinger, Yonina C EldarAbstract:Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manner. To enhance the Target Recovery performance of FAR in complex electromagnetic environments, and particularly, its range-Doppler Recovery performance, multi-Carrier AgilE phaSed Array Radar (CAESAR) was proposed. CAESAR extends FAR to multi-carrier waveforms while introducing the notion of spatial agility. In this paper, we theoretically analyze the range-Doppler Recovery capabilities of CAESAR. Particularly, we derive conditions which guarantee accurate reconstruction of these range-Doppler parameters. These conditions indicate that by increasing the number of frequencies transmitted in each pulse, CAESAR improves performance over conventional FAR, especially in complex environments where some radar measurements are severely corrupted by interference.
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Theoretical Analysis for Extended Target Recovery in Randomized Stepped Frequency Radars
arXiv: Signal Processing, 2019Co-Authors: Lei Wang, Tianyao Huang, Yimin LiuAbstract:Randomized Stepped Frequency Radar (RSFR) is very attractive for tasks under complex electromagnetic environment. Due to the synthetic high range resolution in RSRFs, a Target usually occupies a series of range cells and is called an extended Target. To reconstruct the range-Doppler information in a RSFR, previous studies based on sparse Recovery mainly exploit the sparsity of the Target scene but do not adequately address the extended-Target characteristics, which exist in many practical applications. Block sparsity, which combines the sparsity and the Target extension, better characterizes a priori knowledge of the Target scene in a wideband RSFR. This paper studies the RSFR range-Doppler reconstruction problem using block sparse Recovery. Particularly, we theoretically analyze the block coherence and spectral norm of the observation matrix in RSFR and build a bound on the parameters of the radar, under which the exact Recovery of the range-Doppler information is guaranteed. Both simulation and field experiment results demonstrate the superiority of the block sparse Recovery over conventional sparse Recovery in RSFRs.
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Theoretical Analysis for Extended Target Recovery Using RSFRs
2019 IEEE International Conference on Signal Information and Data Processing (ICSIDP), 2019Co-Authors: Lei Wang, Tianyao Huang, Yimin Liu, Huaiying TanAbstract:Recent years, sparse Recovery that exploits the sparsity of the Target scene has been introduced for range-velocity estimation in the Randomized Stepped Frequency Radar (RSFR). Due to the synthesized high range resolution in RSFRs, a Target usually occupies a series of range cells and is referred to as an extended Target. This clustering characteristic as well as the sparsity of the Target scene appear as the block-sparsity property. By leveraging the block-sparsity property, which better characterizes the priori knowledge of the extended Target than the conventional sparsity property, block sparse Recovery promises to outperform the conventional sparse Recovery. This paper studies the RSFR range-velocity estimation using block sparse Recovery, and theoretically analyzes the Recovery condition. The simulation results demonstrate the superiority of the block sparse Recovery over conventional sparse Recovery in range-velocity reconstruction with RSFRs.
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TenDSuR: Tensor-Based 4D Sub-Nyquist Radar
IEEE Signal Processing Letters, 2019Co-Authors: Kumar Vijay Mishra, Yimin Liu, Yonina C Eldar, Xiqin WangAbstract:We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the Target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-OMP and tensor completion algorithms for signal Recovery. Our numerical experiments demonstrate joint estimation of all three Target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid Target Recovery with respect to tensor-OMP.
Michele Sasdelli - One of the best experts on this subject based on the ideXlab platform.
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Real-time tracker with fast Recovery from Target loss.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Alessandro Bay, Panagiotis Sidiropoulos, Eduard Vazquez, Michele SasdelliAbstract:In this paper, we introduce a variation of a state-of-the-art real-time tracker (CFNet), which adds to the original algorithm robustness to Target loss without a significant computational overhead. The new method is based on the assumption that the feature map can be used to estimate the tracking confidence more accurately. When the confidence is low, we avoid updating the object's position through the feature map; instead, the tracker passes to a single-frame failure mode, during which the patch's low-level visual content is used to swiftly update the object's position, before recovering from the Target loss in the next frame. The experimental evidence provided by evaluating the method on several tracking datasets validates both the theoretical assumption that the feature map is associated to tracking confidence, and that the proposed implementation can achieve Target Recovery in multiple scenarios, without compromising the real-time performance.
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ICASSP - Real-time Tracker with Fast Recovery from Target Loss
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Alessandro Bay, Panagiotis Sidiropoulos, Eduard Vazquez, Michele SasdelliAbstract:In this paper, we introduce a variation of a state-of-the-art real-time tracker (CFNet), which adds to the original algorithm robustness to Target loss without a significant computational overhead. The new method is based on the assumption that the feature map can be used to estimate the tracking confidence more accurately. When the confidence is low, we avoid updating the object’s position through the feature map; instead, the tracker passes to a single-frame failure mode, during which the patch’s low-level visual content is used to swiftly update the object’s position, before recovering from the Target loss in the next frame. The experimental evidence provided by evaluating the method on several tracking datasets validates both the theoretical assumption that the feature map is associated to tracking confidence, and that the proposed implementation can achieve Target Recovery in multiple scenarios, without compromising the real-time performance.
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Hide and Seek tracker: Real-time Recovery from Target loss
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Alessandro Bay, Panagiotis Sidiropoulos, Eduard Vazquez, Michele SasdelliAbstract:In this paper, we examine the real-time Recovery of a video tracker from a Target loss, using information that is already available from the original tracker and without a significant computational overhead. More specifically, before using the tracker output to update the Target position we estimate the detection confidence. In the case of a low confidence, the position update is rejected and the tracker passes to a single-frame failure mode, during which the patch low-level visual content is used to swiftly update the object position, before recovering from the Target loss in the next frame. Orthogonally to this improvement, we further enhance the running average method used for creating the query model in tracking-through-similarity. The experimental evidence provided by evaluation on standard tracking datasets (OTB-50, OTB-100 and OTB-2013) validate that Target Recovery can be successfully achieved without compromising the real-time update of the Target position.
Emre Ertin - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Sparse Target Recovery performance of multi-frequency chirp waveforms
2011Co-Authors: Emre Ertin, Lee C. Potter, Randolph L. MosesAbstract:Imaging radars employ wideband linear frequency modulation (LFM) waveforms to achieve high resolution while maintaining moderate sampling rates through restricting the Target support to a known range interval and using stretch (deramp) processing. In recent work motivated by compressive sensing principles, multi-frequency extensions of the chirp waveforms were proposed to obtain randomized projections of range profiles. This paper considers the sparse Target Recovery problem with chirp transmit waveforms and their multi-frequency extensions. We derive the sensing matrix for multi-frequency chirp waveforms and study its coherence properties. We show that multi-frequency chirp waveforms result in sensing matrices with lower coherence between the columns resulting in improved Target estimation performance compared to the standard LFM waveform.
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Sparse Target Recovery performance of multi-frequency chirp waveforms
2011 19th European Signal Processing Conference, 2011Co-Authors: Emre Ertin, Lee C. Potter, Randolph L. MosesAbstract:Imaging radars employ wideband linear frequency modulation (LFM) waveforms to achieve high resolution while maintaining moderate sampling rates through restricting the Target support to a known range interval and using stretch (deramp) processing. In recent work motivated by compressive sensing principles, multi-frequency extensions of the chirp waveforms were proposed to obtain randomized projections of range profiles. This paper considers the sparse Target Recovery problem with chirp transmit waveforms and their multi-frequency extensions. We derive the sensing matrix for multi-frequency chirp waveforms and study its coherence properties. We show that multi-frequency chirp waveforms result in sensing matrices with lower coherence between the columns resulting in improved Target estimation performance compared to the standard LFM waveform.
