Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179618
Shaik Basheeruddin Shah, Vijay Kumar Chakka
In signal processing applications the information about the signal such as frequency (or) period is known a prior for most of the practical signals like ECG, EEG, speech, etc. Inspired by this, in this paper, we propose a new signal representation to estimate the period and frequency information of a given signal with low computational complexity. We achieve this by representing a finite-length discrete-time signal as a linear combination of signals belongs to Ramanujan subspaces. Further, we evaluate the performance of the proposed representation with a simulated example and also by addressing the problem of reducing Power Line Interference (PLI) in an ECG signal. Finally, for a given integer-valued signal, we show that the computational complexity of the proposed transform is quite low in comparison with the existing transforms, and it is quite comparable for a given real (or) complex-valued signal.
{"title":"Signal Representation Using Ramanujan Subspaces Utilizing A Prior Signal Information","authors":"Shaik Basheeruddin Shah, Vijay Kumar Chakka","doi":"10.1109/SPCOM50965.2020.9179618","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179618","url":null,"abstract":"In signal processing applications the information about the signal such as frequency (or) period is known a prior for most of the practical signals like ECG, EEG, speech, etc. Inspired by this, in this paper, we propose a new signal representation to estimate the period and frequency information of a given signal with low computational complexity. We achieve this by representing a finite-length discrete-time signal as a linear combination of signals belongs to Ramanujan subspaces. Further, we evaluate the performance of the proposed representation with a simulated example and also by addressing the problem of reducing Power Line Interference (PLI) in an ECG signal. Finally, for a given integer-valued signal, we show that the computational complexity of the proposed transform is quite low in comparison with the existing transforms, and it is quite comparable for a given real (or) complex-valued signal.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120988710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179564
Nayan Moni Baishya, P. Bora
Camera model identification is an active research problem because of its importance in investigating the source and the authenticity of an image. Traditional camera model identification methods are based on strategies to extract the low-level traces left by the image acquisition pipeline of a camera on an image. One such intrinsic and camera-specific trace is the sensor pattern noise (SPN). The SPN is roughly approximated from the noise-residual obtained by performing high-pass filtering on an image. The noise-residual of an image also contains information about other types of noises. The extraction of the noise-residuals is generally performed on a single primary color channel, like the green channel of an image. However, the performance of a channel in the YCbCr color space is never explored. In this paper, we have proposed a novel camera model identification method based on convolutional neural network, where the noise-residuals are extracted from the luminance (Y) channel of the images. A constrained convolutional layer learns data-driven high-pass filters to extract the noise-residuals and the following layers learn a feature representation for the classification task. We have conducted experiments with multiple class combinations from the Dresden image database. The experimental results show the effectiveness of the Y channel for camera model identification both in terms of classification accuracy and convergence of the network.
{"title":"Luminance Channel Based Camera Model Identification","authors":"Nayan Moni Baishya, P. Bora","doi":"10.1109/SPCOM50965.2020.9179564","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179564","url":null,"abstract":"Camera model identification is an active research problem because of its importance in investigating the source and the authenticity of an image. Traditional camera model identification methods are based on strategies to extract the low-level traces left by the image acquisition pipeline of a camera on an image. One such intrinsic and camera-specific trace is the sensor pattern noise (SPN). The SPN is roughly approximated from the noise-residual obtained by performing high-pass filtering on an image. The noise-residual of an image also contains information about other types of noises. The extraction of the noise-residuals is generally performed on a single primary color channel, like the green channel of an image. However, the performance of a channel in the YCbCr color space is never explored. In this paper, we have proposed a novel camera model identification method based on convolutional neural network, where the noise-residuals are extracted from the luminance (Y) channel of the images. A constrained convolutional layer learns data-driven high-pass filters to extract the noise-residuals and the following layers learn a feature representation for the classification task. We have conducted experiments with multiple class combinations from the Dresden image database. The experimental results show the effectiveness of the Y channel for camera model identification both in terms of classification accuracy and convergence of the network.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116144963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/spcom50965.2020.9179531
{"title":"SPCOM 2020 Contents","authors":"","doi":"10.1109/spcom50965.2020.9179531","DOIUrl":"https://doi.org/10.1109/spcom50965.2020.9179531","url":null,"abstract":"","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115094572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179523
Vinith Kishore, Subhadip Mukherjee, C. Seelamantula
We consider the problem of reconstructing a complex-valued signal from its phase-only measurements. This framework can be considered as a generalization of the well-known one-bit compressed sensing paradigm where the underlying signal is known to be sparse. In contrast, the proposed formalism does not rely on the assumption of sparsity and hence applies to a broader class of signals. The optimization problem for signal reconstruction is formulated by first splitting the linear measurement vector into its phase and magnitude components and subsequently using the non-negativity property of the magnitude component as a constraint. The resulting optimization problem turns out to be a quadratic program (QP) and is solved using two algorithms: (i) alternating directions method of multipliers; and (ii) projected gradient-descent with Nesterov’s momentum. Due to the inherent scale ambiguity of the phase-only measurement, the underlying signal can be reconstructed only up to a global scale-factor. We obtain high accuracy for reconstructing 1–D synthetic signals in the absence of noise. We also show an application of the proposed approach in reconstructing images from the phase of their measurement coefficients. The underlying image is recovered up to a peak signal-to-noise ratio exceeding 30 dB in several examples, indicating an accurate reconstruction.
{"title":"PhaseSense — Signal Reconstruction from Phase-Only Measurements via Quadratic Programming","authors":"Vinith Kishore, Subhadip Mukherjee, C. Seelamantula","doi":"10.1109/SPCOM50965.2020.9179523","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179523","url":null,"abstract":"We consider the problem of reconstructing a complex-valued signal from its phase-only measurements. This framework can be considered as a generalization of the well-known one-bit compressed sensing paradigm where the underlying signal is known to be sparse. In contrast, the proposed formalism does not rely on the assumption of sparsity and hence applies to a broader class of signals. The optimization problem for signal reconstruction is formulated by first splitting the linear measurement vector into its phase and magnitude components and subsequently using the non-negativity property of the magnitude component as a constraint. The resulting optimization problem turns out to be a quadratic program (QP) and is solved using two algorithms: (i) alternating directions method of multipliers; and (ii) projected gradient-descent with Nesterov’s momentum. Due to the inherent scale ambiguity of the phase-only measurement, the underlying signal can be reconstructed only up to a global scale-factor. We obtain high accuracy for reconstructing 1–D synthetic signals in the absence of noise. We also show an application of the proposed approach in reconstructing images from the phase of their measurement coefficients. The underlying image is recovered up to a peak signal-to-noise ratio exceeding 30 dB in several examples, indicating an accurate reconstruction.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133439124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179630
Ankit, M. Bhatnagar
Dimensionality of the fluid environment plays a crucial role in characterizing the diffusive channel. It is generally believed that increasing the dimensionality of the fluid medium should negatively affect the hitting probabilities as the degrees of freedom of propagating molecules have been enhanced. This paper has twofold objectives, it provides the diffusion channel characterization of a molecular communication (MC) system in an enclosed cuboid geometry and then studies the effect of dimensionality and the size of the receiver on the obtained channel statistics. The motility probability distribution function (PDF) of the molecules in a constrained cuboid environment with five reflecting and one absorbing wall is derived. The first hitting time (FHT) PDF and the hitting probabilities of the molecules to the absorbing wall are deduced from the same. A comparative analytical study of the derived FHT PDF against the diffusion channel statistics of various bounded and unbounded environments is presented. The comparison quantitatively establishes that an MC system with suitably configured fluid boundaries and transmitter and receiver arrangement can completely eliminate the effect of dimensionality and the size of the receiver on the hitting probabilities. The study may be of use in designing practically efficient and economic MC systems.
{"title":"Diffusion Channel Characterization for A Cuboid Container: Some Insights into The Role of Dimensionality and Fluid Boundaries","authors":"Ankit, M. Bhatnagar","doi":"10.1109/SPCOM50965.2020.9179630","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179630","url":null,"abstract":"Dimensionality of the fluid environment plays a crucial role in characterizing the diffusive channel. It is generally believed that increasing the dimensionality of the fluid medium should negatively affect the hitting probabilities as the degrees of freedom of propagating molecules have been enhanced. This paper has twofold objectives, it provides the diffusion channel characterization of a molecular communication (MC) system in an enclosed cuboid geometry and then studies the effect of dimensionality and the size of the receiver on the obtained channel statistics. The motility probability distribution function (PDF) of the molecules in a constrained cuboid environment with five reflecting and one absorbing wall is derived. The first hitting time (FHT) PDF and the hitting probabilities of the molecules to the absorbing wall are deduced from the same. A comparative analytical study of the derived FHT PDF against the diffusion channel statistics of various bounded and unbounded environments is presented. The comparison quantitatively establishes that an MC system with suitably configured fluid boundaries and transmitter and receiver arrangement can completely eliminate the effect of dimensionality and the size of the receiver on the hitting probabilities. The study may be of use in designing practically efficient and economic MC systems.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130425554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179566
K. S. S. Anudeep, Kuldeep Khoria, R. Das
For identifying sparse systems, a recently proposed algorithm called upper threshold based zero attracting proportionate normalized least mean square (UT-ZA-PNLMS) algorithm has shown improved performance in terms of both the convergence rate and steady-state error in comparison to the ZAPNLMS algorithm. The UT-ZA-PNLMS algorithm employs adaptive threshold based gain function in order to improve convergence rate of the active taps, especially the taps with low magnitude, and appends zero attracting term in the update equation in order to bring the inactive taps to their optimum zero level. However, as the UT-ZA-PNLMS algorithm uses uniform shrinkage for that zero attraction, the active taps experience significant bias which limits overall steady-state performance. In this paper, we introduce selective shrinkage for the zero attracting term so that the inactive taps get strong attractive force whereas the active taps would experience negligibly small attractive force, and thus the bias in the active tap is reduced. In particular, we propose three different algorithms incorporating log-sum, $ell_{p^{-}}$ norm and $ell_{0}$-norm penalties to the cost function of the upper threshold based PNLMS algorithm. The resulting algorithms are studied extensively and the simulation results show their improved steady-state performances.
{"title":"Improving Steady-State Performance of the UT-ZA-PNLMS Algorithm for Sparse Systems","authors":"K. S. S. Anudeep, Kuldeep Khoria, R. Das","doi":"10.1109/SPCOM50965.2020.9179566","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179566","url":null,"abstract":"For identifying sparse systems, a recently proposed algorithm called upper threshold based zero attracting proportionate normalized least mean square (UT-ZA-PNLMS) algorithm has shown improved performance in terms of both the convergence rate and steady-state error in comparison to the ZAPNLMS algorithm. The UT-ZA-PNLMS algorithm employs adaptive threshold based gain function in order to improve convergence rate of the active taps, especially the taps with low magnitude, and appends zero attracting term in the update equation in order to bring the inactive taps to their optimum zero level. However, as the UT-ZA-PNLMS algorithm uses uniform shrinkage for that zero attraction, the active taps experience significant bias which limits overall steady-state performance. In this paper, we introduce selective shrinkage for the zero attracting term so that the inactive taps get strong attractive force whereas the active taps would experience negligibly small attractive force, and thus the bias in the active tap is reduced. In particular, we propose three different algorithms incorporating log-sum, $ell_{p^{-}}$ norm and $ell_{0}$-norm penalties to the cost function of the upper threshold based PNLMS algorithm. The resulting algorithms are studied extensively and the simulation results show their improved steady-state performances.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134262498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179526
M. Sivasankar, R. Hegde
Development of angular superresolution methods for resolving targets using multifunction phased array radar is challenging. Angular superresolution of closely spaced coherent targets with strong interferences in the context of phased array radar has hitherto not been addressed. In this paper a novel beamforming method with angular superresolution is proposed for resolving closely spaced coherent targets in the presence of interferences. A dynamic subarray beamforming framework is first developed based on the knowledge of the number of interferences. The output obtained from the dynamic subarray beamformer is then smoothed using an augmented covariance method to account for the coherence of targets. Superresolution method is then used to obtain robust DOA estimates even at low SNR. Experiments on DOA estimation are conducted in typical target detection scenarios and the results are evaluated using several performance metrics to illustrate the significance of the proposed method.
{"title":"Dynamic Subarray Beamforming for Angular Superresolution of Coherent Targets","authors":"M. Sivasankar, R. Hegde","doi":"10.1109/SPCOM50965.2020.9179526","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179526","url":null,"abstract":"Development of angular superresolution methods for resolving targets using multifunction phased array radar is challenging. Angular superresolution of closely spaced coherent targets with strong interferences in the context of phased array radar has hitherto not been addressed. In this paper a novel beamforming method with angular superresolution is proposed for resolving closely spaced coherent targets in the presence of interferences. A dynamic subarray beamforming framework is first developed based on the knowledge of the number of interferences. The output obtained from the dynamic subarray beamformer is then smoothed using an augmented covariance method to account for the coherence of targets. Superresolution method is then used to obtain robust DOA estimates even at low SNR. Experiments on DOA estimation are conducted in typical target detection scenarios and the results are evaluated using several performance metrics to illustrate the significance of the proposed method.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"36 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124507378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179596
Ashok Bandi, R. B. S. Mysore, S. Chatzinotas, B. Ottersten
This paper studies the joint design of user scheduling and precoding for the maximization of spectral efficiency (SE) for a multigroup multicast scenario in multiuser MISO downlink channels. Noticing that the existing definition of SE fails to account for group sizes, a new metric called multicast spectral efficiency (MC-SE) is proposed. In this context, the joint design is considered for the maximization of MC-SE. Firstly, with the help of binary scheduling variables, the joint design problem is formulated as a mixed-integer non-linear programming problem such that it facilitates the joint update of scheduling and precoding variables. Further, useful reformulations are proposed to reveal the hidden difference-of-convex/concave structure of the problem. Thereafter, we propose a convex-concave procedure based iterative algorithm with convergence guarantees to a stationary point. Finally, we compare different aspects namely MC-SE, SE and number of scheduled users through Monte-Carlo simulations.
{"title":"Joint User Scheduling, and Precoding for Multicast Spectral Efficiency in Multigroup Multicast Systems","authors":"Ashok Bandi, R. B. S. Mysore, S. Chatzinotas, B. Ottersten","doi":"10.1109/SPCOM50965.2020.9179596","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179596","url":null,"abstract":"This paper studies the joint design of user scheduling and precoding for the maximization of spectral efficiency (SE) for a multigroup multicast scenario in multiuser MISO downlink channels. Noticing that the existing definition of SE fails to account for group sizes, a new metric called multicast spectral efficiency (MC-SE) is proposed. In this context, the joint design is considered for the maximization of MC-SE. Firstly, with the help of binary scheduling variables, the joint design problem is formulated as a mixed-integer non-linear programming problem such that it facilitates the joint update of scheduling and precoding variables. Further, useful reformulations are proposed to reveal the hidden difference-of-convex/concave structure of the problem. Thereafter, we propose a convex-concave procedure based iterative algorithm with convergence guarantees to a stationary point. Finally, we compare different aspects namely MC-SE, SE and number of scheduled users through Monte-Carlo simulations.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116650142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179595
Rishu Raj, A. Dixit
Visible light communication (VLC) is an enabling technology which provides ubiquitous indoor wireless access but the capacity of VLC systems is severely limited by the narrow modulation bandwidth of the light emitting diode (LED) transmitters. Non-orthogonal multiple access (NOMA) is envisioned to address this challenge by circumventing the need for spectrum allocation (as in orthogonal multiple access schemes) which limits the number of users. We formulate an analytical model to evaluate the total system capacity achievable by using NOMA in a multiple-input-multiple-output (MIMO) VLC system. We analyze the total capacity of a NOMA based $2 times 2$ MIMO-VLC system employing two different power allocation schemes, namely, gain ratio power allocation (GRPA) and normalized gain power allocation (NGDPA), for power domain superposition coding at the transmitter. We evaluate and compare the performance of these two schemes for various system parameters like system coverage, user location and number of users. The results and analyses presented herein provide critical insights into the modelling of NOMA based MIMO-VLC systems.
{"title":"Performance Evaluation of Power Allocation Schemes for Non-Orthogonal Multiple Access in MIMO Visible Light Communication Links","authors":"Rishu Raj, A. Dixit","doi":"10.1109/SPCOM50965.2020.9179595","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179595","url":null,"abstract":"Visible light communication (VLC) is an enabling technology which provides ubiquitous indoor wireless access but the capacity of VLC systems is severely limited by the narrow modulation bandwidth of the light emitting diode (LED) transmitters. Non-orthogonal multiple access (NOMA) is envisioned to address this challenge by circumventing the need for spectrum allocation (as in orthogonal multiple access schemes) which limits the number of users. We formulate an analytical model to evaluate the total system capacity achievable by using NOMA in a multiple-input-multiple-output (MIMO) VLC system. We analyze the total capacity of a NOMA based $2 times 2$ MIMO-VLC system employing two different power allocation schemes, namely, gain ratio power allocation (GRPA) and normalized gain power allocation (NGDPA), for power domain superposition coding at the transmitter. We evaluate and compare the performance of these two schemes for various system parameters like system coverage, user location and number of users. The results and analyses presented herein provide critical insights into the modelling of NOMA based MIMO-VLC systems.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130394911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/SPCOM50965.2020.9179549
Harsh Vardhan, Ruchi Tripathi, K. Rajawat
This work proposes a dynamic matrix completion (DMC)-based approach for use in the front-end of MIMO radar. The proposed approach is different and complementary to the conventional target tracking algorithms that are widely deployed in the back-end of radar systems. The received signals are modelled as time-varying low-rank matrices and passed through an adaptive singular value thresholding (ASVT) block, resulting in the elimination of noise returns early in the processing chain. When all the antenna elements are not being used and the received signal is only partially observed, the ASVT block imputes the missing entries. Front-end processing results in cleaner signals for the back-end, culminating in fewer range and Doppler bins, increased probability of detection, reduced false alarm rate, and ultimately, improved target tracking performance. Detailed simulation of the radar chain reveal the significant improvements afforded by the proposed algorithm.
{"title":"Adaptive Front-end for MIMO Radar with Dynamic Matrix Completion","authors":"Harsh Vardhan, Ruchi Tripathi, K. Rajawat","doi":"10.1109/SPCOM50965.2020.9179549","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179549","url":null,"abstract":"This work proposes a dynamic matrix completion (DMC)-based approach for use in the front-end of MIMO radar. The proposed approach is different and complementary to the conventional target tracking algorithms that are widely deployed in the back-end of radar systems. The received signals are modelled as time-varying low-rank matrices and passed through an adaptive singular value thresholding (ASVT) block, resulting in the elimination of noise returns early in the processing chain. When all the antenna elements are not being used and the received signal is only partially observed, the ASVT block imputes the missing entries. Front-end processing results in cleaner signals for the back-end, culminating in fewer range and Doppler bins, increased probability of detection, reduced false alarm rate, and ultimately, improved target tracking performance. Detailed simulation of the radar chain reveal the significant improvements afforded by the proposed algorithm.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131015824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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