Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646372
Sylvain Cluzel, M. Dervin, J. Radzik, Sonia Cazalens, C. Baudoin, D. Dragomirescu
One of the main issues in using a Low Earth Orbit (LEO) satellite constellation to extend a Low-Powered Wide Area Network is the frequency synchronization. Using a link based on random access solves this concern, but also prevents delivery guarantees, and implies less predictable performance. This paper concerns the estimation of Bit Error Rate (BER) and Packet Error Rate (PER) using physical layer abstractions under a time and frequency random scheme, namely Time and Frequency Aloha. We first derive a BER calculation for noncoded QPSK transmission with one collision. Then, we use the 3GPP LTE NB-IoT coding scheme. We analyze the interference that could be induced by repetition coding scheme and propose an efficient summation to improve the decoder performance. Finally, to estimate a PER for any collided scenario, we propose a physical layer abstraction, which relies on an equivalent Signal-to-Noise Ratio (SNR) calculation based on Mutual Information.
利用低地球轨道(LEO)卫星星座扩展低功率广域网的主要问题之一是频率同步。使用基于随机访问的链接解决了这个问题，但也阻止了交付保证，并且意味着更不可预测的性能。本文研究了在时间和频率随机方案(time and frequency Aloha)下，利用物理层抽象来估计误码率(BER)和包错误率(PER)。我们首先推导了具有一次碰撞的非编码QPSK传输的误码率计算。然后，我们使用3GPP LTE NB-IoT编码方案。我们分析了重复编码方案可能引起的干扰，并提出了一种有效的求和方法来提高解码器的性能。最后，为了估计任何碰撞场景的PER，我们提出了一种物理层抽象，它依赖于基于互信息的等效信噪比(SNR)计算。
{"title":"PHYSICAL LAYER ABSTRACTION FOR PERFORMANCE EVALUATION OF LEO SATELLITE SYSTEMS FOR IOT USING TIME-FREQUENCY ALOHA SCHEME","authors":"Sylvain Cluzel, M. Dervin, J. Radzik, Sonia Cazalens, C. Baudoin, D. Dragomirescu","doi":"10.1109/GLOBALSIP.2018.8646372","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646372","url":null,"abstract":"One of the main issues in using a Low Earth Orbit (LEO) satellite constellation to extend a Low-Powered Wide Area Network is the frequency synchronization. Using a link based on random access solves this concern, but also prevents delivery guarantees, and implies less predictable performance. This paper concerns the estimation of Bit Error Rate (BER) and Packet Error Rate (PER) using physical layer abstractions under a time and frequency random scheme, namely Time and Frequency Aloha. We first derive a BER calculation for noncoded QPSK transmission with one collision. Then, we use the 3GPP LTE NB-IoT coding scheme. We analyze the interference that could be induced by repetition coding scheme and propose an efficient summation to improve the decoder performance. Finally, to estimate a PER for any collided scenario, we propose a physical layer abstraction, which relies on an equivalent Signal-to-Noise Ratio (SNR) calculation based on Mutual Information.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125605759","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646446
Harsha Sridhar, J. Kumar, S. Jois, C. Seelamantula
We present a novel method for fitting an ellipse to scattered data based on least-squares minimization. The new technique has several advantages over the standard ellipse fitting techniques. For one, it is constraint-free and computationally inexpensive thus making it easy to implement. Also, despite the absence of constraints, execution of the model always results in an ellipse fit. Additionally, the model results in a singular solution for a given set of datapoints. The proposed model is compared with standard techniques and shown to have the ability to fit an accurate ellipse even when other methods either fail to be ellipse-specific or take up excessive computation time for execution. An application to the problem of segmentation of the optic cup in retinal fundus images, is also presented. Experimental validation and performance comparisons show that the proposed technique is competitive with the state-of-the-art methods.
{"title":"AN UNCONSTRAINED ELLIPSE FITTING TECHNIQUE AND APPLICATION TO OPTIC CUP SEGMENTATION","authors":"Harsha Sridhar, J. Kumar, S. Jois, C. Seelamantula","doi":"10.1109/GlobalSIP.2018.8646446","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646446","url":null,"abstract":"We present a novel method for fitting an ellipse to scattered data based on least-squares minimization. The new technique has several advantages over the standard ellipse fitting techniques. For one, it is constraint-free and computationally inexpensive thus making it easy to implement. Also, despite the absence of constraints, execution of the model always results in an ellipse fit. Additionally, the model results in a singular solution for a given set of datapoints. The proposed model is compared with standard techniques and shown to have the ability to fit an accurate ellipse even when other methods either fail to be ellipse-specific or take up excessive computation time for execution. An application to the problem of segmentation of the optic cup in retinal fundus images, is also presented. Experimental validation and performance comparisons show that the proposed technique is competitive with the state-of-the-art methods.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122624980","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646580
Raied Caromi, M. Souryal, Wen-Bin Yang
In the 3.5 GHz Citizens Broadband Radio Service (CBRS), 100 MHz of spectrum will be shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. This paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of matched-filter detectors for these sensors. We find that the proposed detectors exceed the requirements for performance in the presence of co-channel interference from commercial long term evolution (LTE) signals, meaning that more commercial devices can use the band in the proximity of sensors. Furthermore, the detectors are robust to out-of-band emissions into this band from adjacent-band radars, which prior studies have found can be significant.
{"title":"Detection of Incumbent Radar in the 3.5 GHZ CBRS Band","authors":"Raied Caromi, M. Souryal, Wen-Bin Yang","doi":"10.1109/GlobalSIP.2018.8646580","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646580","url":null,"abstract":"In the 3.5 GHz Citizens Broadband Radio Service (CBRS), 100 MHz of spectrum will be shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. This paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of matched-filter detectors for these sensors. We find that the proposed detectors exceed the requirements for performance in the presence of co-channel interference from commercial long term evolution (LTE) signals, meaning that more commercial devices can use the band in the proximity of sensors. Furthermore, the detectors are robust to out-of-band emissions into this band from adjacent-band radars, which prior studies have found can be significant.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123877253","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646336
N. Ito, T. Nakatani
Here we introduce multiplicative update rules for full-rank spatial covariance analysis (FCA), a blind source separation (BSS) method proposed by Duong et al. ["Under-determined reverberant audio source separation using a full-rank spatial covariance model," IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830–1840, Sept. 2010]. In the FCA, source separation is performed by multichannel Wiener filtering with the covariance matrix of each source signal estimated by the expectation-maximization (EM) algorithm. A drawback of this EM algorithm is that it does not necessarily yield good covariance matrix estimates within a feasible number of iterations. In contrast, the proposed multiplicative update rules tend to give covariance matrix estimates that result in better source separation performance than the EM algorithm. Furthermore, we propose joint diagonalization based acceleration of the multiplicative update rules, which leads to signifi-cantly reduced computation time per iteration. In a BSS experiment, the proposed multiplicative update rules resulted in higher source separation performance than the conventional EM algorithm overall. Moreover, the joint diagonalization based accelerated algorithm was up to 200 times faster than the algorithm without acceleration, which is realized without much degradation in the source separation performance.
在这里，我们引入了全秩空间协方差分析(FCA)的乘法更新规则，这是Duong等人提出的一种盲源分离(BSS)方法。美国手语协会，第18卷，第18期。7, pp. 1830-1840, Sept. 2010]。在FCA中，通过多通道维纳滤波实现源分离，每个源信号的协方差矩阵由期望最大化(EM)算法估计。这种EM算法的一个缺点是，它不一定在可行的迭代次数内产生良好的协方差矩阵估计。相比之下，所提出的乘法更新规则倾向于给出协方差矩阵估计，从而比EM算法具有更好的源分离性能。此外，我们提出了基于联合对角化的乘法更新规则加速，从而显著减少了每次迭代的计算时间。在BSS实验中，所提出的乘法更新规则总体上比传统的EM算法具有更高的源分离性能。此外，基于联合对角化的加速算法比没有加速的算法快200倍，并且在不降低源分离性能的情况下实现。
{"title":"MULTIPLICATIVE UPDATES AND JOINT DIAGONALIZATION BASED ACCELERATION FOR UNDER-DETERMINED BSS USING A FULL-RANK SPATIAL COVARIANCE MODEL","authors":"N. Ito, T. Nakatani","doi":"10.1109/GlobalSIP.2018.8646336","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646336","url":null,"abstract":"Here we introduce multiplicative update rules for full-rank spatial covariance analysis (FCA), a blind source separation (BSS) method proposed by Duong et al. [\"Under-determined reverberant audio source separation using a full-rank spatial covariance model,\" IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830–1840, Sept. 2010]. In the FCA, source separation is performed by multichannel Wiener filtering with the covariance matrix of each source signal estimated by the expectation-maximization (EM) algorithm. A drawback of this EM algorithm is that it does not necessarily yield good covariance matrix estimates within a feasible number of iterations. In contrast, the proposed multiplicative update rules tend to give covariance matrix estimates that result in better source separation performance than the EM algorithm. Furthermore, we propose joint diagonalization based acceleration of the multiplicative update rules, which leads to signifi-cantly reduced computation time per iteration. In a BSS experiment, the proposed multiplicative update rules resulted in higher source separation performance than the conventional EM algorithm overall. Moreover, the joint diagonalization based accelerated algorithm was up to 200 times faster than the algorithm without acceleration, which is realized without much degradation in the source separation performance.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121247881","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646585
K. Guan, B. Ai, Danping He, D. Matolak, Qi Wang, Z. Zhong, T. Kürner
In this paper, we model obstructed vehicle-to-vehicle (V2V) channels for the 5-GHz band through measurement-validated ray-tracing (RT) simulations. To begin, we establish a realistic V2V RT simulator through integrating three key channel features: small-scale structures (e.g. lampposts, traffic signs), handled by their approximate radar cross sections; large-scale structures (such as buildings and ground), calibrating their electromagnetic and scattering parameters; and obstructing vehicle effects via V2V channel measurements. Then, based on extensive RT simulations, the target channels are characterized comprehensively. All the parameters are input into and verified by the 3GPP-like quasi deterministic radio channel generator (QuaDRiGa). By adding the obstructed V2V scenario into standard channel model families, this paper provides a foundation for evaluating intelligent vehicular communications in challenging conditions.
{"title":"OBSTRUCTED VEHICLE-TO-VEHICLE CHANNEL MODELING FOR INTELLIGENT VEHICULAR COMMUNICATIONS","authors":"K. Guan, B. Ai, Danping He, D. Matolak, Qi Wang, Z. Zhong, T. Kürner","doi":"10.1109/GLOBALSIP.2018.8646585","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646585","url":null,"abstract":"In this paper, we model obstructed vehicle-to-vehicle (V2V) channels for the 5-GHz band through measurement-validated ray-tracing (RT) simulations. To begin, we establish a realistic V2V RT simulator through integrating three key channel features: small-scale structures (e.g. lampposts, traffic signs), handled by their approximate radar cross sections; large-scale structures (such as buildings and ground), calibrating their electromagnetic and scattering parameters; and obstructing vehicle effects via V2V channel measurements. Then, based on extensive RT simulations, the target channels are characterized comprehensively. All the parameters are input into and verified by the 3GPP-like quasi deterministic radio channel generator (QuaDRiGa). By adding the obstructed V2V scenario into standard channel model families, this paper provides a foundation for evaluating intelligent vehicular communications in challenging conditions.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128699600","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646547
Chia-Wei Huang, Chung-An Shen, T. Chin, Shan-Hsiang Shen
It is crucial to achieve high reliability and low latency concurrently for networked applications such as smart grid, data center, and intelligent factory. Furthermore, maintaining low complexity and energy efficiency for network devices incurs another dimension of challenges. Based on Software Defined Networking (SDN), this paper presents a link recovery mechanism which enhances the reliability of the network, maintains low communication latency, and reduces memory utilizations of the switching devices. To be specific, for achieving low latency, the protection-based link recovery approach is employed where the backup paths are pre-installed in the switch. Furthermore, an improved Segment Routing (SR) approach is utilized where the path information is encoded in the packet header for reducing the flow states stored in the switch. The experimental results show that, achieving real-time link recovery, the proposed mechanism leads to a 25% saving in memory utilizations and thus greatly reduces the complexity of the switch. The energy efficiency of the switch, and the entire network, can be enhanced accordingly.
{"title":"A Real-time and Memory-saving Link Recovery Mechanism for Green Software-Defined Networking","authors":"Chia-Wei Huang, Chung-An Shen, T. Chin, Shan-Hsiang Shen","doi":"10.1109/GlobalSIP.2018.8646547","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646547","url":null,"abstract":"It is crucial to achieve high reliability and low latency concurrently for networked applications such as smart grid, data center, and intelligent factory. Furthermore, maintaining low complexity and energy efficiency for network devices incurs another dimension of challenges. Based on Software Defined Networking (SDN), this paper presents a link recovery mechanism which enhances the reliability of the network, maintains low communication latency, and reduces memory utilizations of the switching devices. To be specific, for achieving low latency, the protection-based link recovery approach is employed where the backup paths are pre-installed in the switch. Furthermore, an improved Segment Routing (SR) approach is utilized where the path information is encoded in the packet header for reducing the flow states stored in the switch. The experimental results show that, achieving real-time link recovery, the proposed mechanism leads to a 25% saving in memory utilizations and thus greatly reduces the complexity of the switch. The energy efficiency of the switch, and the entire network, can be enhanced accordingly.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203749","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646539
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li
Classifying severely occluded images is a challenging yet highly-needed task. In this paper, motivated by the fact that human being can exploit context information to assist learning, we apply convolutional recurrent neural network (CRNN) to attack this challenging problem. A CRNN architecture that integrates convolutional neural network (CNN) with long short-term memory (LSTM) is presented. Three new datasets with severely occluded images and context information are created. Extensive experiments are conducted to compare the performance of CRNN against conventional methods and human experimenters. The experiment results show that the CRNN outperforms both conventional methods and most of the human experimenters. This demonstrates that CRNN can effectively learn and exploit the unspecified context information among image sequences, and thus can be an effective approach to resolve the challenging problem of classifying severely occluded images.
{"title":"CLASSIFICATION OF SEVERELY OCCLUDED IMAGE SEQUENCES VIA CONVOLUTIONAL RECURRENT NEURAL NETWORKS","authors":"Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li","doi":"10.1109/GLOBALSIP.2018.8646539","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646539","url":null,"abstract":"Classifying severely occluded images is a challenging yet highly-needed task. In this paper, motivated by the fact that human being can exploit context information to assist learning, we apply convolutional recurrent neural network (CRNN) to attack this challenging problem. A CRNN architecture that integrates convolutional neural network (CNN) with long short-term memory (LSTM) is presented. Three new datasets with severely occluded images and context information are created. Extensive experiments are conducted to compare the performance of CRNN against conventional methods and human experimenters. The experiment results show that the CRNN outperforms both conventional methods and most of the human experimenters. This demonstrates that CRNN can effectively learn and exploit the unspecified context information among image sequences, and thus can be an effective approach to resolve the challenging problem of classifying severely occluded images.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116216564","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646676
Hui Chen, Tarig Ballal, T. Al-Naffouri
Direction of arrival (DOA) information of a signal is important in communications, localization, object tracking and so on. Frequency-domain-based time-delay estimation is capable of achieving DOA in subsample accuracy; however, it suffers from the phase wrapping problem. In this paper, a frequency-diversity based method is proposed to overcome the phase wrapping problem. Inspired by the machine learning technique of random ferns, an algorithm is proposed to speed up the search procedure. The performance of the algorithm is evaluated based on three different signal models using both simulations and experimental tests. The results show that using random ferns can reduce search time to 1/6 of the search time of the exhaustive method while maintaining the same accuracy. The proposed search approach outperforms a benchmark frequency-diversity based algorithm by offering lower DOA estimation error.
{"title":"FAST PHASE-DIFFERENCE-BASED DOA ESTIMATION USING RANDOM FERNS","authors":"Hui Chen, Tarig Ballal, T. Al-Naffouri","doi":"10.1109/GlobalSIP.2018.8646676","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646676","url":null,"abstract":"Direction of arrival (DOA) information of a signal is important in communications, localization, object tracking and so on. Frequency-domain-based time-delay estimation is capable of achieving DOA in subsample accuracy; however, it suffers from the phase wrapping problem. In this paper, a frequency-diversity based method is proposed to overcome the phase wrapping problem. Inspired by the machine learning technique of random ferns, an algorithm is proposed to speed up the search procedure. The performance of the algorithm is evaluated based on three different signal models using both simulations and experimental tests. The results show that using random ferns can reduce search time to 1/6 of the search time of the exhaustive method while maintaining the same accuracy. The proposed search approach outperforms a benchmark frequency-diversity based algorithm by offering lower DOA estimation error.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161403","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 : 2018-11-01DOI: 10.1109/globalsip.2018.8646572
{"title":"GlobalSIP 2018 TOC","authors":"","doi":"10.1109/globalsip.2018.8646572","DOIUrl":"https://doi.org/10.1109/globalsip.2018.8646572","url":null,"abstract":"","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586624","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646513
Saurabh Sihag, A. Tajer
This paper considers the problem of non-linear state estimation in power systems when the system model is not known with certainty due to lack of complete information about the model or possible disruptions in the network. Specifically, this paper focuses on the settings in which the true model might deviate from the nominal model to a group of alternative models. Such uncertainty in the true model adds another dimension to the system state estimation. Specifically, the state estimator must also detect if the system model has deviated from the nominal model, and then isolate the true model. The estimation and detection/isolation decisions are intertwined as the estimation performance is linked with the detection/isolation decisions, but isolation of the true model is never perfect due to noisy measurements. This paper establishes this fundamental interplay between model isolation and state estimation, and characterizes the optimal state estimator and model detector.
{"title":"NON-LINEAR STATE ESTIMATION IN POWER SYSTEMS UNDER MODEL UNCERTAINTY","authors":"Saurabh Sihag, A. Tajer","doi":"10.1109/GlobalSIP.2018.8646513","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646513","url":null,"abstract":"This paper considers the problem of non-linear state estimation in power systems when the system model is not known with certainty due to lack of complete information about the model or possible disruptions in the network. Specifically, this paper focuses on the settings in which the true model might deviate from the nominal model to a group of alternative models. Such uncertainty in the true model adds another dimension to the system state estimation. Specifically, the state estimator must also detect if the system model has deviated from the nominal model, and then isolate the true model. The estimation and detection/isolation decisions are intertwined as the estimation performance is linked with the detection/isolation decisions, but isolation of the true model is never perfect due to noisy measurements. This paper establishes this fundamental interplay between model isolation and state estimation, and characterizes the optimal state estimator and model detector.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126221842","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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