Pub Date : 2015-09-01DOI: 10.1109/SSPD.2015.7288519
R. Radhakrishnan, Abhinoy Kumar Singh, S. Bhaumik, Nutan Kumar Tomar
A typical underwater passive bearings-only target tracking problem is solved using nonlinear filters namely cubature Kalman filter (CKF), Gauss-Hermite filter (GHF) and sparse-grid Gauss-Hermite filter (SGHF). The performance of the filters is compared in terms of estimation accuracy, track-loss count and computational time. Theoretical Cramer-Rao lower bound (CRLB) is used to determine the maximum achievable performance and to compare the error bounds of various filters used.
{"title":"Quadrature Filters for Underwater Passive Bearings-Only Target Tracking","authors":"R. Radhakrishnan, Abhinoy Kumar Singh, S. Bhaumik, Nutan Kumar Tomar","doi":"10.1109/SSPD.2015.7288519","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288519","url":null,"abstract":"A typical underwater passive bearings-only target tracking problem is solved using nonlinear filters namely cubature Kalman filter (CKF), Gauss-Hermite filter (GHF) and sparse-grid Gauss-Hermite filter (SGHF). The performance of the filters is compared in terms of estimation accuracy, track-loss count and computational time. Theoretical Cramer-Rao lower bound (CRLB) is used to determine the maximum achievable performance and to compare the error bounds of various filters used.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122910339","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288520
Xiao Dong, Yunhua Zhang
In this paper, we consider the problem of radar imaging with quantized data. The quantized CS (QCS) method is used to reconstruct the radar image of sparse targets from quantized data. The reconstruction problem is derived in the maximum a posteriori (MAP) estimation framework and formulated as a convex optimization problem. We compare the proposed method with the traditional l1-regularization method using 1-D simulated data with different quantization bits. For coarse quantization with 1 or 2 bits, the simulation results show that the QCS method outperforms the l1- regularization method in high SNR situations. For high- resolution quantization with more bits, we derive the conditions under which the l1-regularization method and the QCS method are equivalent. This statement is explained theoretically and confirmed by simulation results.
{"title":"Radar Imaging with Quantized Measurements Based on Compressed Sensing","authors":"Xiao Dong, Yunhua Zhang","doi":"10.1109/SSPD.2015.7288520","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288520","url":null,"abstract":"In this paper, we consider the problem of radar imaging with quantized data. The quantized CS (QCS) method is used to reconstruct the radar image of sparse targets from quantized data. The reconstruction problem is derived in the maximum a posteriori (MAP) estimation framework and formulated as a convex optimization problem. We compare the proposed method with the traditional l1-regularization method using 1-D simulated data with different quantization bits. For coarse quantization with 1 or 2 bits, the simulation results show that the QCS method outperforms the l1- regularization method in high SNR situations. For high- resolution quantization with more bits, we derive the conditions under which the l1-regularization method and the QCS method are equivalent. This statement is explained theoretically and confirmed by simulation results.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127372800","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288505
C. Blair, J. Thompson, N. Robertson
Gaussian Process classification (GPC) allows accurate and reliable detection of objects. The high computational load of squared-error or radial basis function kernels limits the applications that GPC can be used in, as memory requirements and computation time are both limiting factors. We describe our version of accelerated GPC on GPU (Graphics Processing Unit). GPUs have limited memory so any GPC implementation must be memory-efficient as well as computationally efficient. Using a high-performance pedestrian detector as a starting point, we use its packed or block-based feature descriptor and demonstrate a fast matrix multiplication implementation of GPC which is also extremely memory efficient. We demonstrate a speed up of 3.7 times over a multicore, BLAS-optimised CPU implementation. Results show that this is more accurate and reliable than results obtained from a comparable support vector machine algorithm.
{"title":"GPU-Accelerated Gaussian Processes for Object Detection","authors":"C. Blair, J. Thompson, N. Robertson","doi":"10.1109/SSPD.2015.7288505","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288505","url":null,"abstract":"Gaussian Process classification (GPC) allows accurate and reliable detection of objects. The high computational load of squared-error or radial basis function kernels limits the applications that GPC can be used in, as memory requirements and computation time are both limiting factors. We describe our version of accelerated GPC on GPU (Graphics Processing Unit). GPUs have limited memory so any GPC implementation must be memory-efficient as well as computationally efficient. Using a high-performance pedestrian detector as a starting point, we use its packed or block-based feature descriptor and demonstrate a fast matrix multiplication implementation of GPC which is also extremely memory efficient. We demonstrate a speed up of 3.7 times over a multicore, BLAS-optimised CPU implementation. Results show that this is more accurate and reliable than results obtained from a comparable support vector machine algorithm.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115068305","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288527
A. Radius, P. Marques
A new algorithm for the velocity vector estimation of moving ships using Single Look Complex (SLC) SAR data in strip map acquisition mode is proposed. The algorithm exploits both amplitude and phase information of the Doppler decompressed data spectrum, with the aim to estimate both the azimuth antenna pattern and the backscattering coefficient as function of the look angle. The antenna pattern estimation provides information about the target velocity; the backscattering coefficient can be used for vessel classification. The range velocity is retrieved in the slow time frequency domain by estimating the antenna pattern effects induced by the target motion, while the azimuth velocity is calculated by the estimated range velocity and the ship orientation. Finally, the algorithm is tested on simulated SAR SLC data.
{"title":"Velocity Estimation of Moving Ships Using C-Band SLC SAR Data","authors":"A. Radius, P. Marques","doi":"10.1109/SSPD.2015.7288527","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288527","url":null,"abstract":"A new algorithm for the velocity vector estimation of moving ships using Single Look Complex (SLC) SAR data in strip map acquisition mode is proposed. The algorithm exploits both amplitude and phase information of the Doppler decompressed data spectrum, with the aim to estimate both the azimuth antenna pattern and the backscattering coefficient as function of the look angle. The antenna pattern estimation provides information about the target velocity; the backscattering coefficient can be used for vessel classification. The range velocity is retrieved in the slow time frequency domain by estimating the antenna pattern effects induced by the target motion, while the azimuth velocity is calculated by the estimated range velocity and the ship orientation. Finally, the algorithm is tested on simulated SAR SLC data.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"27 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131549458","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288518
T. Iwamoto
There are increasing demands for radio systems to work in more hostile conditions these days. Communication protocols used already by many users, however, need considerable efforts to be modified to increase resistance against deception. Identification technologies of emitters are expected to offer an additional layer to prevent fraudulent devices from accessing wireless systems. They have been developed to distinguish emitters even of the same product based only on emitted signals as well as to handle a large variety of signals efficiently. In this paper a hierarchical identification method consists of a classification of signals into subclasses and an identification of signals in the same subclass is presented; modulation symbol sequences are utilized to classify Automatic Identification System signals into subclasses efficiently and an identifier of a set of binary support vector machines is trained on one of the hardest subclass of signals emitted by the six similar emitters mounted on the six boats. These are the largest number of boats servicing in the same regular line in Japan. Experimental results of identification show practical mean accuracy of 97.6% under a controlled S/N, which corresponds to that of signals sampled over a distance of 100 km.
{"title":"Practical Identification of Specific Emitters Used in the Automatic Identification System","authors":"T. Iwamoto","doi":"10.1109/SSPD.2015.7288518","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288518","url":null,"abstract":"There are increasing demands for radio systems to work in more hostile conditions these days. Communication protocols used already by many users, however, need considerable efforts to be modified to increase resistance against deception. Identification technologies of emitters are expected to offer an additional layer to prevent fraudulent devices from accessing wireless systems. They have been developed to distinguish emitters even of the same product based only on emitted signals as well as to handle a large variety of signals efficiently. In this paper a hierarchical identification method consists of a classification of signals into subclasses and an identification of signals in the same subclass is presented; modulation symbol sequences are utilized to classify Automatic Identification System signals into subclasses efficiently and an identifier of a set of binary support vector machines is trained on one of the hardest subclass of signals emitted by the six similar emitters mounted on the six boats. These are the largest number of boats servicing in the same regular line in Japan. Experimental results of identification show practical mean accuracy of 97.6% under a controlled S/N, which corresponds to that of signals sampled over a distance of 100 km.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133584264","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288522
Marian Andrecki, E. Delande, J. Houssineau, Daniel E. Clark
This paper investigates a sensor management scheme that aims at minimising the regional variance in the number of objects present in regions of interest whilst performing multi-target filtering with the PHD filter. The experiments are conducted in a simulated environment with groups of targets moving through a scene in order to inspect the behaviour of the manager. The results demonstrate that computing the variance in the number of objects in different regions provides a viable means of increasing situational awareness where complete coverage is not possible. A discussion follows, highlighting the limitations of the PHD filter and discussing the applicability of the proposed method to alternative available approaches in multi-object filtering.
{"title":"Sensor Management with Regional Statistics for the PHD Filter","authors":"Marian Andrecki, E. Delande, J. Houssineau, Daniel E. Clark","doi":"10.1109/SSPD.2015.7288522","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288522","url":null,"abstract":"This paper investigates a sensor management scheme that aims at minimising the regional variance in the number of objects present in regions of interest whilst performing multi-target filtering with the PHD filter. The experiments are conducted in a simulated environment with groups of targets moving through a scene in order to inspect the behaviour of the manager. The results demonstrate that computing the variance in the number of objects in different regions provides a viable means of increasing situational awareness where complete coverage is not possible. A discussion follows, highlighting the limitations of the PHD filter and discussing the applicability of the proposed method to alternative available approaches in multi-object filtering.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123504448","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288515
Watcharapong Ketpan, Seksan Phonsri, Rongrong Qian, M. Sellathurai
The passive radar also known as Green Radar exploits the available commercial communication signals and is useful for target tracking and detection in general. Recent communications standards frequently employ Orthogonal Frequency Division Multiplexing (OFDM) waveforms and wideband for broadcasting. This paper focuses on the recent developments of the target detection algorithms in the OFDM passive radar framework where its channel estimates have been derived using the matched filter concept using the knowledge of the transmitted signals. The MUSIC algorithm, which has been modified to solve this two dimensional delay-Doppler detection problem, is first reviewed. As the target detection problem can be represented as sparse signals, this paper employs compressive sensing to compare with the detection capability of the 2-D MUSIC algorithm. It is found that the previously proposed single time sample compressive sensing cannot significantly reduce the leakage from the direct signal component. Furthermore, this paper proposes the compressive sensing method utilizing multiple time samples, namely l1-SVD, for the detection of multiple targets. In comparison between the MUSIC and compressive sensing, the results show that l1-SVD can decrease the direct signal leakage but its prerequisite of computational resources remains a major issue. This paper also presents the detection performance of these two algorithms for closely spaced targets.
{"title":"On the Target Detection in OFDM Passive Radar Using MUSIC and Compressive Sensing","authors":"Watcharapong Ketpan, Seksan Phonsri, Rongrong Qian, M. Sellathurai","doi":"10.1109/SSPD.2015.7288515","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288515","url":null,"abstract":"The passive radar also known as Green Radar exploits the available commercial communication signals and is useful for target tracking and detection in general. Recent communications standards frequently employ Orthogonal Frequency Division Multiplexing (OFDM) waveforms and wideband for broadcasting. This paper focuses on the recent developments of the target detection algorithms in the OFDM passive radar framework where its channel estimates have been derived using the matched filter concept using the knowledge of the transmitted signals. The MUSIC algorithm, which has been modified to solve this two dimensional delay-Doppler detection problem, is first reviewed. As the target detection problem can be represented as sparse signals, this paper employs compressive sensing to compare with the detection capability of the 2-D MUSIC algorithm. It is found that the previously proposed single time sample compressive sensing cannot significantly reduce the leakage from the direct signal component. Furthermore, this paper proposes the compressive sensing method utilizing multiple time samples, namely l1-SVD, for the detection of multiple targets. In comparison between the MUSIC and compressive sensing, the results show that l1-SVD can decrease the direct signal leakage but its prerequisite of computational resources remains a major issue. This paper also presents the detection performance of these two algorithms for closely spaced targets.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125277619","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288526
P. Feng, Wenwu Wang, S. M. Naqvi, J. Chambers
We propose a robust particle probability hypothesis density (PHD) filter where the variational Bayesian method is applied in joint recursive prediction of the state and the time varying measurement noise parameters. The proposed particle PHD filter is based on forming variational approximation to the joint distribution of states and noise parameters at each frame separately; the state is estimated with a particle PHD filter and the measurement noise variances used in the update step are estimated with a fixed point iteration approach. A deep belief network (DBN) is used in the update step to mitigate the effect of measurement noise on the calculation of particle weights in each frame. The deep learning network is trained based on both colour and oriented gradient histogram (HOG) features and then used to mitigate the measurement noise from the particle selection step, thereby improving the tracking performance. Simulation results using sequences from the CAVIAR dataset show the improvements of the proposed DBN aided variational Bayesian particle PHD filter over the traditional particle PHD filter.
{"title":"Variational Bayesian PHD Filter with Deep Learning Network Updating for Multiple Human Tracking","authors":"P. Feng, Wenwu Wang, S. M. Naqvi, J. Chambers","doi":"10.1109/SSPD.2015.7288526","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288526","url":null,"abstract":"We propose a robust particle probability hypothesis density (PHD) filter where the variational Bayesian method is applied in joint recursive prediction of the state and the time varying measurement noise parameters. The proposed particle PHD filter is based on forming variational approximation to the joint distribution of states and noise parameters at each frame separately; the state is estimated with a particle PHD filter and the measurement noise variances used in the update step are estimated with a fixed point iteration approach. A deep belief network (DBN) is used in the update step to mitigate the effect of measurement noise on the calculation of particle weights in each frame. The deep learning network is trained based on both colour and oriented gradient histogram (HOG) features and then used to mitigate the measurement noise from the particle selection step, thereby improving the tracking performance. Simulation results using sequences from the CAVIAR dataset show the improvements of the proposed DBN aided variational Bayesian particle PHD filter over the traditional particle PHD filter.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324193","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288528
Y. Pailhas, Y. Pétillot
Multiple Input Multiple Output (MIMO) sonar systems offer new perspectives for target detection and underwater surveillance. The inherent principle of MIMO relies on transmitting several pulses from different transmitters. The MIMO waveform strategy can vary from applications to applications. But among the waveform space, orthogonal waveforms are arguably the most important sub-space. Purely orthogonal waveforms do not exist, and several approximations have been attempted for MIMO radar applications. These approaches include separating the waveforms in the time domain, the frequency domain or using pseudo orthogonal codes. In this paper we discuss the different radar waveform approaches from a sonar point of view and propose a novel CDMA (code division multiple access) waveform design, more suitable for large wideband MIMO systems.
{"title":"Wideband CDMA Waveforms for Large MIMO Sonar Systems","authors":"Y. Pailhas, Y. Pétillot","doi":"10.1109/SSPD.2015.7288528","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288528","url":null,"abstract":"Multiple Input Multiple Output (MIMO) sonar systems offer new perspectives for target detection and underwater surveillance. The inherent principle of MIMO relies on transmitting several pulses from different transmitters. The MIMO waveform strategy can vary from applications to applications. But among the waveform space, orthogonal waveforms are arguably the most important sub-space. Purely orthogonal waveforms do not exist, and several approximations have been attempted for MIMO radar applications. These approaches include separating the waveforms in the time domain, the frequency domain or using pseudo orthogonal codes. In this paper we discuss the different radar waveform approaches from a sonar point of view and propose a novel CDMA (code division multiple access) waveform design, more suitable for large wideband MIMO systems.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134415636","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 : 2015-09-01DOI: 10.1109/SSPD.2015.7288517
André Sandmann, A. Ahrens, S. Lochmann
Singular-value decomposition (SVD) is well-established in multiple-input multiple-output (MIMO) signal processing where a broadband MIMO channel is transformed into a number of weighted single-input single-output (SISO) channels. However, applying SVD to frequency-selective MIMO channels results in unequally weighted SISO channels requiring complex resource allocation techniques for optimizing the channel performance. Therefore, a different approach utilizing polynomial matrix singular-value decomposition (PMSVD) for removing the MIMO interference is studied, outperforming conventional SVD-based MIMO systems in the analyzed channel scenarios. As shown by the bit-error rate (BER) simulation results as well as the obtained spectral efficiencies, the proposed PMSVD-based solution seems to be a good alternative to conventional SVD-based MIMO systems.
{"title":"Performance Analysis of Polynomial Matrix SVD-Based Broadband MIMO Systems","authors":"André Sandmann, A. Ahrens, S. Lochmann","doi":"10.1109/SSPD.2015.7288517","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288517","url":null,"abstract":"Singular-value decomposition (SVD) is well-established in multiple-input multiple-output (MIMO) signal processing where a broadband MIMO channel is transformed into a number of weighted single-input single-output (SISO) channels. However, applying SVD to frequency-selective MIMO channels results in unequally weighted SISO channels requiring complex resource allocation techniques for optimizing the channel performance. Therefore, a different approach utilizing polynomial matrix singular-value decomposition (PMSVD) for removing the MIMO interference is studied, outperforming conventional SVD-based MIMO systems in the analyzed channel scenarios. As shown by the bit-error rate (BER) simulation results as well as the obtained spectral efficiencies, the proposed PMSVD-based solution seems to be a good alternative to conventional SVD-based MIMO systems.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114333424","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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