Wind power generation has gradually developed into an important approach of energy supply. Meanwhile, due to the difficulty of electricity storage, wind power is greatly affected by the real-time wind speed in wind fields. Generally, wind speed has the characteristics of nonlinear, irregular, and non-stationary, which make accurate wind speed forecasting a difficult problem. Recent studies have shown that ensemble forecasting approaches combining different sub-models is an efficient way to solve the problem. Therefore, in this article, two single models are ensembled for wind speed forecasting. Meanwhile, four data pre-processing hybrid models are combined with the reliability weights. The proposed ensemble approaches are simulated on the real wind speed data in the Longdong area of Loess Plateau in China from 2007 to 2015, the experimental results indicate that the ensemble approaches outperform individual models and other hybrid models with different pre-processing methods.
{"title":"Ensemble Neural Network Method for Wind Speed Forecasting","authors":"Binbin Yong, Fei Qiao, Chen Wang, Jun Shen, Yongqiang Wei, Qingguo Zhou","doi":"10.1109/SiPS47522.2019.9020410","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020410","url":null,"abstract":"Wind power generation has gradually developed into an important approach of energy supply. Meanwhile, due to the difficulty of electricity storage, wind power is greatly affected by the real-time wind speed in wind fields. Generally, wind speed has the characteristics of nonlinear, irregular, and non-stationary, which make accurate wind speed forecasting a difficult problem. Recent studies have shown that ensemble forecasting approaches combining different sub-models is an efficient way to solve the problem. Therefore, in this article, two single models are ensembled for wind speed forecasting. Meanwhile, four data pre-processing hybrid models are combined with the reliability weights. The proposed ensemble approaches are simulated on the real wind speed data in the Longdong area of Loess Plateau in China from 2007 to 2015, the experimental results indicate that the ensemble approaches outperform individual models and other hybrid models with different pre-processing methods.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137657","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020534
Xun Liu, M. Dohler
The emerging Internet of Skills that exchanges tactile and other sensorial data, significantly augments traditional multimedia. The increase of data scale and modalities demands for codecs dedicated to these sensorial data. In this paper, we propose a codec for compression of vibrotactile data in the spirit of Weber’s law. To be specific, a companding function is applied to the vibrotactile data, so that the quantisation step of high amplitude is larger than that of low amplitude. The curve of the companding function is optimised through a data-driven approach. To evaluate the performance of the vibrotactile codec in terms of human perceived quality, rigorous subjective tests are conducted. The results demonstrate that 75% compression of vibrotactile data is achieved without perceivable degradation. More importantly, the computational complexity is much lower and the latency performance is superior, compared with other vibrotactile codecs. The computational complexity of the proposed codec is about 1/20 of that of previous codecs, while the time delay is approximately 1/30 of that of previous codec.
{"title":"A Data-Driven Approach to Vibrotactile Data Compression","authors":"Xun Liu, M. Dohler","doi":"10.1109/SiPS47522.2019.9020534","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020534","url":null,"abstract":"The emerging Internet of Skills that exchanges tactile and other sensorial data, significantly augments traditional multimedia. The increase of data scale and modalities demands for codecs dedicated to these sensorial data. In this paper, we propose a codec for compression of vibrotactile data in the spirit of Weber’s law. To be specific, a companding function is applied to the vibrotactile data, so that the quantisation step of high amplitude is larger than that of low amplitude. The curve of the companding function is optimised through a data-driven approach. To evaluate the performance of the vibrotactile codec in terms of human perceived quality, rigorous subjective tests are conducted. The results demonstrate that 75% compression of vibrotactile data is achieved without perceivable degradation. More importantly, the computational complexity is much lower and the latency performance is superior, compared with other vibrotactile codecs. The computational complexity of the proposed codec is about 1/20 of that of previous codecs, while the time delay is approximately 1/30 of that of previous codec.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747547","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020555
Wenjian Liu, Xiayuan Wen, Jun Lin, Zhongfeng Wang, L. Du
Efficient quantization techniques can compress Convolutional Neural Networks (CNNs) with less bit-width while maintaining the accuracy on large extent. However, the quantized CNN models hardly boost the computation performance of CNN accelerators with the conventional bit-parallel Multiply-Accumulate (MAC) operations. Previous works proposed a shifting-based bit-serial operation, which can be called as Shift-Accumulate (SAC) operation, to take advantage of the reduced bit-width. However, it is also found that there are many invalid computations in both MAC and SAC operation, caused by zero bits in activations and weights, which are not optimized. To fully exploit the computations in CNN models, we proposed a Essen-tial Address only GAC based Low-latency Efficient (EAGLE) architecture that can further accelerate the CNN computation through bypassing zero bits computation in the activations and weights. An essential address is adopted to encode the nonzero bits in activations and weights in this architecture. Furthermore, to support the essential address-only computations, Generate-Accumulate (GAC), an operation which produces partial sums with essential addresses, is implemented. The architecture is implemented with a TSMC 28nm CMOS technology. Based on the results, if scaled in a 65nm technology, the EAGLE only requires 63.6% area and 43.1% power consumption compare to that of the Pragmatic. The EAGLE reaches an average speedup of $ 2.08times$ and $ 1.43times$ on six CNN models over the Stripe and Pragmatic at a similar frequency, respectively. It also improves energy efficiency by $ 3.69times$ on average over the DaDianNao baseline.
{"title":"EAGLE: Exploiting Essential Address in Both Weight and Activation to Accelerate CNN Computing","authors":"Wenjian Liu, Xiayuan Wen, Jun Lin, Zhongfeng Wang, L. Du","doi":"10.1109/SiPS47522.2019.9020555","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020555","url":null,"abstract":"Efficient quantization techniques can compress Convolutional Neural Networks (CNNs) with less bit-width while maintaining the accuracy on large extent. However, the quantized CNN models hardly boost the computation performance of CNN accelerators with the conventional bit-parallel Multiply-Accumulate (MAC) operations. Previous works proposed a shifting-based bit-serial operation, which can be called as Shift-Accumulate (SAC) operation, to take advantage of the reduced bit-width. However, it is also found that there are many invalid computations in both MAC and SAC operation, caused by zero bits in activations and weights, which are not optimized. To fully exploit the computations in CNN models, we proposed a Essen-tial Address only GAC based Low-latency Efficient (EAGLE) architecture that can further accelerate the CNN computation through bypassing zero bits computation in the activations and weights. An essential address is adopted to encode the nonzero bits in activations and weights in this architecture. Furthermore, to support the essential address-only computations, Generate-Accumulate (GAC), an operation which produces partial sums with essential addresses, is implemented. The architecture is implemented with a TSMC 28nm CMOS technology. Based on the results, if scaled in a 65nm technology, the EAGLE only requires 63.6% area and 43.1% power consumption compare to that of the Pragmatic. The EAGLE reaches an average speedup of $ 2.08times$ and $ 1.43times$ on six CNN models over the Stripe and Pragmatic at a similar frequency, respectively. It also improves energy efficiency by $ 3.69times$ on average over the DaDianNao baseline.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122619907","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020315
Zhongchi Fang, Zheng Cao, Lan Wang
The rank-reduction (RARE) algorithm is a well-known class of algorithms for direction of arrival (DOA) es-timation in the presence of imperfect array manifolds. Since the spectral peak search is inevitable for the current RARE algorithm, it may bring a huge occupational load for practical implementations. In order to reduce the computational com-plexity, in this paper, we propose a root-RARE algorithm for DOA estimation with partly calibrated uniform linear arrays (ULAs). Through replacing the spectral peak search with a polynomial root finding, our proposed method can get much higher efficiency than the original RARE method. Simulation results demonstrate that our method can significantly reduce the computational complexity and improve the DOA estimation performance in a low SNR case.
{"title":"A Root-RARE Algorithm for DOA Estimation with Partly Calibrated Uniform Linear Arrays","authors":"Zhongchi Fang, Zheng Cao, Lan Wang","doi":"10.1109/SiPS47522.2019.9020315","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020315","url":null,"abstract":"The rank-reduction (RARE) algorithm is a well-known class of algorithms for direction of arrival (DOA) es-timation in the presence of imperfect array manifolds. Since the spectral peak search is inevitable for the current RARE algorithm, it may bring a huge occupational load for practical implementations. In order to reduce the computational com-plexity, in this paper, we propose a root-RARE algorithm for DOA estimation with partly calibrated uniform linear arrays (ULAs). Through replacing the spectral peak search with a polynomial root finding, our proposed method can get much higher efficiency than the original RARE method. Simulation results demonstrate that our method can significantly reduce the computational complexity and improve the DOA estimation performance in a low SNR case.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124218693","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020508
Yilan Li, Mingyang Li, A. Sanyal, Yanzhi Wang, Qinru Qiu
Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power.
{"title":"Autonomous UAV with Learned Trajectory Generation and Control","authors":"Yilan Li, Mingyang Li, A. Sanyal, Yanzhi Wang, Qinru Qiu","doi":"10.1109/SiPS47522.2019.9020508","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020508","url":null,"abstract":"Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128498235","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020375
Haojing Hu, Rongke Liu, Baoping Feng
Polar codes, as the first channel code that can provably achieve the channel capacity, have received increasing attention these years. However, in practical application, the decoding of polar codes still has many aspects that need improvement. One of the key bottlenecks of polar codes decoding is the high latency of SC (Successive Cancellation) -based decoding algorithms. As one of the solutions to this problem, many SCL (Successive Cancellation List) decoding algorithms with the multi-bit decision are proposed. Despite of the reduction of decoding latency, the complexity spent for computation and sort of path candidates of these algorithms has significantly increased in contrast with the conventional SCL algorithm. In this paper, we propose a novel SCL decoding algorithm with multi-bit decision for polar codes, named as Flexible and Simplified Multi-bit Successive Cancellation List (FSMSCL) decoding. The proposed algorithm further reduces the latency compared to other existing multi-bit decoding algorithms. On the other hand, we provide different path-splitting schemes for different code blocks to control the complexity of computation and sort of path metrics. In the analysis, the experiment results show that the proposed algorithm has similar FER performance compared to the conventional SCL algorithm but its decoding latency outperforms other peer decoding algorithms with multi-bit decision.
{"title":"Flexible and Simplified Multi-bit Successive-Cancellation List Decoding for Polar Codes","authors":"Haojing Hu, Rongke Liu, Baoping Feng","doi":"10.1109/SiPS47522.2019.9020375","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020375","url":null,"abstract":"Polar codes, as the first channel code that can provably achieve the channel capacity, have received increasing attention these years. However, in practical application, the decoding of polar codes still has many aspects that need improvement. One of the key bottlenecks of polar codes decoding is the high latency of SC (Successive Cancellation) -based decoding algorithms. As one of the solutions to this problem, many SCL (Successive Cancellation List) decoding algorithms with the multi-bit decision are proposed. Despite of the reduction of decoding latency, the complexity spent for computation and sort of path candidates of these algorithms has significantly increased in contrast with the conventional SCL algorithm. In this paper, we propose a novel SCL decoding algorithm with multi-bit decision for polar codes, named as Flexible and Simplified Multi-bit Successive Cancellation List (FSMSCL) decoding. The proposed algorithm further reduces the latency compared to other existing multi-bit decoding algorithms. On the other hand, we provide different path-splitting schemes for different code blocks to control the complexity of computation and sort of path metrics. In the analysis, the experiment results show that the proposed algorithm has similar FER performance compared to the conventional SCL algorithm but its decoding latency outperforms other peer decoding algorithms with multi-bit decision.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116885171","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020501
Qisheng Huang, Ming Jiang, Chunming Zhao
This paper proposes a novel constellation design in AWGN channel through learning based auto-encoder (AE). Additionally, this paper illustrates the reason why learning based constellation has better performance than the classical square-shaped QAM design by analyzing the Euclidean distance distribution and the bound of symbol error rate between learning designed symbols and other constellations. Moreover, the performance of learning based constellation will be compared to constellation based on convex optimization design. To solve the bit mapping problem of the learning based constellation, $Q-$ary LDPC encoding is applied to these specifically designed QAM modulation systems, where the soft decoding of $Q-$ary LDPC codes can be carried out with the symbol-level soft outputs of demodulation.
{"title":"Learning to Design Constellation for AWGN Channel Using Auto-Encoders","authors":"Qisheng Huang, Ming Jiang, Chunming Zhao","doi":"10.1109/SiPS47522.2019.9020501","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020501","url":null,"abstract":"This paper proposes a novel constellation design in AWGN channel through learning based auto-encoder (AE). Additionally, this paper illustrates the reason why learning based constellation has better performance than the classical square-shaped QAM design by analyzing the Euclidean distance distribution and the bound of symbol error rate between learning designed symbols and other constellations. Moreover, the performance of learning based constellation will be compared to constellation based on convex optimization design. To solve the bit mapping problem of the learning based constellation, $Q-$ary LDPC encoding is applied to these specifically designed QAM modulation systems, where the soft decoding of $Q-$ary LDPC codes can be carried out with the symbol-level soft outputs of demodulation.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114239135","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020507
Zengchao Yan, Wenjie Li, Jun Lin, Zhongfeng Wang
Reed-Solomon (RS) codes are widely adopted in numerous digital communication systems to handle the possibly occurred errors and/or erasures during the data transmission. This paper focuses on the $t=2$ RS codes and proposes a low-complexity error-and-erasure decoding algorithm for them. The proposed algorithm directly computes the errata location polynomial instead of performing the iterative Berlekmap-Massey (BM) algorithm which is usually adopted in the conventional RS decoding algorithm. Moreover, a method to directly compute the errata locations and errata magnitudes is also presented. For a (255,251) RS code, the proposed error-and-erasure decoding algorithm can save over 90% multiplications and additions of the conventional decoding algorithm. In addition, the complexity reduction becomes more significant as code length increases.
{"title":"A Low-Complexity Error-and-Erasure Decoding Algorithm for t=2 RS Codes","authors":"Zengchao Yan, Wenjie Li, Jun Lin, Zhongfeng Wang","doi":"10.1109/SiPS47522.2019.9020507","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020507","url":null,"abstract":"Reed-Solomon (RS) codes are widely adopted in numerous digital communication systems to handle the possibly occurred errors and/or erasures during the data transmission. This paper focuses on the $t=2$ RS codes and proposes a low-complexity error-and-erasure decoding algorithm for them. The proposed algorithm directly computes the errata location polynomial instead of performing the iterative Berlekmap-Massey (BM) algorithm which is usually adopted in the conventional RS decoding algorithm. Moreover, a method to directly compute the errata locations and errata magnitudes is also presented. For a (255,251) RS code, the proposed error-and-erasure decoding algorithm can save over 90% multiplications and additions of the conventional decoding algorithm. In addition, the complexity reduction becomes more significant as code length increases.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"28 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120823143","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}
Frequency modulated continuous wave (FMCW) radar has been widely used and thoroughly studied in high precision ranging. However, FMCW radar cannot identify target type while ranging. To address this problem, this paper presents a high-precision ranging method based on FMCW, which enables the FMCW radar to identify target type while ranging. The proposed method improves the ranging accuracy by performing frequency sweep in a specific frequency range and realizes target identification through the orthogonal spread spectrum. The designed prototype verification system operates at a frequency of 5.8 GHz and the sweep bandwidth is 200 MHz. Exemplary experiment results are presented to illustrate that the system has a ranging accuracy of 20 cm and a range of up to 12 m in an indoor scenario. Furthermore, multiple user identification can also be realized.
{"title":"An FMCW Ranging Method with Identification Ability","authors":"Meiqing Liu, Shang Ma, Boen Chi, Kai Long, Qiu Huang, Bixin Zhu","doi":"10.1109/SiPS47522.2019.9020417","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020417","url":null,"abstract":"Frequency modulated continuous wave (FMCW) radar has been widely used and thoroughly studied in high precision ranging. However, FMCW radar cannot identify target type while ranging. To address this problem, this paper presents a high-precision ranging method based on FMCW, which enables the FMCW radar to identify target type while ranging. The proposed method improves the ranging accuracy by performing frequency sweep in a specific frequency range and realizes target identification through the orthogonal spread spectrum. The designed prototype verification system operates at a frequency of 5.8 GHz and the sweep bandwidth is 200 MHz. Exemplary experiment results are presented to illustrate that the system has a ranging accuracy of 20 cm and a range of up to 12 m in an indoor scenario. Furthermore, multiple user identification can also be realized.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134022783","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 : 2019-10-01DOI: 10.1109/sips47522.2019.9020633
{"title":"SiPS 2019 Message","authors":"","doi":"10.1109/sips47522.2019.9020633","DOIUrl":"https://doi.org/10.1109/sips47522.2019.9020633","url":null,"abstract":"","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134212880","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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