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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Kaiqiang Ren Zhengbo Sun National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781510845541

This paper proposed a novel calibration algorithm for sensor position errors of moving sensors by using calibration emitters. The algorithm presented is based on time difference of arrival(TDOA) measurements received by sensor network with sensor position error. Firstly, the linear relation between sensor position errors and TDOA measurements is derived by Taylor's series expansion. Then, a linear minimum mean square error(LMMSE) estimator of sensor position errors is derived based on the linear model. At last, on the assumption that sensor position errors of moving sensors are steady in a short time, a sequential LMMSE algorithm for sensor position errors estimation is proposed. Simulation results demonstrate the good performance of the proposed algorithm.

关键词： sensor position errors moving sensors time difference of arrival(TDOA) calibration emitters sequential linear minimun mean square error(LMMSE)

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Direct Position Determination by Double Fixed Station Based on Delay and AOA

Direct Position Determination by Double Fixed Station Based ...

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Xiangyu Liu Qing He Zhengbo Sun National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781510845541

To improve location accuracy, a single-step localization algorithm by double fixed station, using the thought of "signal to position" is proposed to solve the problem of two-step conventional method's information loss, since two-step conventional method divides in estimating intermediate parameter and geolocation. First, the observed signal model is analyzed and problem's mathematical model is generalized. Next, the cost function is formulated based on maximum likelihood estimator(MLE) and simplified as the maximal eigenvalue of hermite matrix. Then, the geographical location maps in twodimensional sector-grid based on angel of arrival(AOA), afterward, the algorithm process is introduced. Finally, simulation results demonstrated that the proposed DPD algorithm outperforms the two-step conventional algorithm in location accuracy, and when signal to noise ratio(SNR) of the same observed signals is-5 dB, the root mean squared error(RMSE) of proposed algorithm reduce the errors of 47% in typical scene.

关键词： passive localization direct position determination(DPD) maximum likelihood estimator(MLE) passive localization time delay angel of arrival(AOA)

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Modeling and analysis of connected elements interferometry error influencing factors

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Journal of Beijing Institute of technology 2016年第S2期25卷 166-169页

作者：赵培焱王斌王帆 National Key Laboratory of Science and Technology on Blind Signal Processing

Space security is facing unprecedented challenges with more and more crowded *** elements interferometry(CEI) is a new radio interference measurement technology,which can achieve high precision of angle measurement on a short baseline,it is expected to be independent or combined with other method to open up a new area of orbit ***,the influence of time delay measurement and baseline length on angle estimation are analyzed;secondly,the theoretical modeling of the propagation error is carried out,and then the influence of the ionosphere and troposphere are simulated and analyzed;finally,the error of the measurement equipment is modeled,then the clock synchronization error,frequency error and location error are analyzed *** results have important effects on the next research and engineering application of CEI.

关键词： space security connected elements interferometry(CEI) theoretical modeling accuracy analysis

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A space debris detection method based on distributed radar

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Journal of Beijing Institute of technology 2016年第S2期25卷 170-174页

作者：宋科康赵培焱王斌彭华峰 National Key Laboratory of Science and Technology on Blind Signal Processing

The radar echoes of space debris are weak and difficult to be *** at this problem,a distributed radar observation model is built combined with orbit characteristics of space debris,and a centralized CA-CFAR(constant false alarm rate) detection method with incoherent integration is proposed for space debris ***,the space debris scattering property and echoes model are ***,the multistation centralized cooperative detection system is applied,and as the space noise environment is well relatively,a CA-CFAR detector is adopted that follows the traditional incoherent integration,which solves the problem that echoes parameter can’t be estimated correctly and is propitious to space debris *** with four radars shows that the method can improve detection probability of space debris by 40% compared with one radar when the SNR is 10 dB.

关键词： centralized detection constant false alarm rate(CFAR) space debris distributed radar

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Using Indirect Communications to Improve Relationship Strength Estimation

Using Indirect Communications to Improve Relationship Streng...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zeng Chen Keren Wang National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

The estimation of relationship strength based on phone communication data has drawn great interests from amounts of researches. This paper focuses on combining indirect communications with direct communications to improve relationship strength estimation. Firstly, algorithms for finding one-jump and two-jump indirect communications are proposed to make preparations for feature extraction. Then features from direct and indirect communications are extracted and aggregated to be used for classification. Experiment results on MIT Social Relationship dataset indicate that our method combining direct and indirect communications performs better than existing method. Especially, our method improves the performance of estimating relationship strength on pairs with no direct communications.

关键词： Feature extraction Mobile handsets Estimation Mobile communication Integrated circuits Electronic mail Social network services

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An Improved Decomposition Multiobjective Optimization Algorithm with Weight Vector Adaptation Strategy

An Improved Decomposition Multiobjective Optimization Algori...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zhixiang Li Liang He Yanjie Chu National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems, and solves each subproblem in a collaborative manner. For a MOP, different subproblems often have different difficulty to be approximated, especially when the MOP is extremely complex or has a discontinuous optimal front. This paper proposes a weight vector adaptation strategy for this issue, which changes the weight vectors and optimizes their computational resource allocation to fit the MOP. The experimental results on a variety of MOP test instances show that the proposed algorithm is competitive in comparison with three state-of-the-art decomposition multiobjective evolutionary algorithms.

关键词： Sociology Statistics Optimization Evolutionary computation Algorithm design and analysis Resource management Signal processing algorithms

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Learning robust features from underwater ship-radiated noise with mutual information group sparse DBN 45

Learning robust features from underwater ship-radiated noise...

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45th International Congress and Exposition on Noise Control Engineering: Towards a Quieter Future, INTER-NOISE 2016

作者： Shen, Sheng Yang, Honghui Han, Zhen Shi, Junjun Xiong, Jinyu Zhang, Xiaoyong School of Marine Science and Technology Northwestern Polytechnical University China Science and Technology on Blind Signals Processing Laboratory China

Classifying Underwater Acoustic Target(UAT) based on ship-radiated noise datasets is a small-sample-size classification problem. Generalization ability of features depending on prior knowledge and traditional signal processing is seriously restricted for lack of labeled data. Deep Belief Networks (DBN) can make use of abundant unlabeled data and extract features spontaneously, but the resulting features exhibit statistical dependencies. In this paper, we propose a feature learning model called mutual information group sparse DBN (MIGS-DBN) for ship-radiated noise. Firstly, a standard RBM is pre-trained in an unsupervised learning way, then the hidden units are grouped adaptively according to mutual information and the activation level are punished. Secondly, the improved RBMs are stacked by performing a greedy layer-wise training phase, followed by the discriminative fine-tuning to achieve the sparse representation of multilayer features. Recognition experiments on real-world ship-radiated noise show that, features learned by the proposed model can reduce the redundancy and improve classification performance significantly. © 2016, German Acoustical Society (DEGA). All rights reserved.

关键词： Signal processing

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Modulation recognition of underwater acoustic communication signals based on denoising & deep sparse autoencoder 45

Modulation recognition of underwater acoustic communication ...

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45th International Congress and Exposition on Noise Control Engineering: Towards a Quieter Future, INTER-NOISE 2016

作者： Yang, Honghui Yao, Xiaohui Shen, Sheng Xiong, Jinyu Zhang, Xiaoyong School of Marine Science and Technology Northwestern Polytechnical University China Science and Technology on Blind Signals Processing Laboratory China

It is difficult to identify the modulation types of underwater acoustic communication signals via traditional modulation recognition methods since the received communication signals always have a low signal to noise ratio (SNR) due to the oceanic ambient noise and signal distortion. In this paper, we propose a modulation recognition model for underwater acoustic communication signals which is combined by a denoising autoencoder and a deep sparse autoencoder. We use a denoising autoencoder to reconstruct the signals as a preprocessing in order to enhance the signals and then we use the reconstructed signals as training datasets to train a deep sparse autoencoder to classify 4 kinds of modulation types of underwater acoustic communication signals. Denoising autoencoder can learn essential representations of input that are robust to stochastic corruption in input, besides, sparse coding can make the representations learning more efficient. Comparative experiments clearly show the advantage of using denoising autoencoder and deep sparse autoencoder in modulation recognition of underwater acoustic communication signals. © 2016, German Acoustical Society (DEGA). All rights reserved.

关键词： Modulation

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Dynamic Range Impact on 3D MIMO Channel Characteristics in Rural-Macro Scenario at 3.5 GHz

Dynamic Range Impact on 3D MIMO Channel Characteristics in R...

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European Conference on Antennas and Propagation

作者： Weite Zhang Lei Tian Zhongyuan Wu Jianhua Zhang Yi Zheng Jingmin Cao Key Lab of Universal Wireless Communications Ministry of Education State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Mailbox NO.92 100876 Beijing China China Mobile Research Institude Beijing China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781785618154;9781785618161

Rural-macro (RMa) is an important scenario for fifth generation (5G) deployment, and there is a strong diffraction in RMa making it difficult to distinguish between line-of-sight (LoS) and non-line-of-sight (NLoS) case. Since the environmental condition is closely related to the dynamic range of channel impulse response (CIR), this paper studies the dynamic range impact on characteristics of three dimensional (3D) multiple-input multiple-output (MIMO) channels based on 3.5 GHz measurement campaign in RMa. The space-alternating generalized expectation-maximization (SAGE) algorithm is employed to estimate multipath component parameters. The channel statistical characteristics, channel capacity and eigenvalues of CIR matrix are calculated for different CIR dynamic ranges. The results show that the delay spread, the angle spread, the channel capacity and the sub-channel correlation are linearly related to the dynamic range of CIR. Thus, the results give a new perspective to study channel characteristics based on CIR dynamic range in RMa scenario.

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A Novel Clustering Method Based on Density Peaks and Its Validation by Channel Measurement

A Novel Clustering Method Based on Density Peaks and Its Val...

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European Conference on Antennas and Propagation

作者： Hanqing Xue Lei Tian Yuxiang Zhang Chao Wang Jianhua Zhang Wei Li Hewen Wei Key Lab of Universal Wireless Communications Ministry of Education State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications P. O. Box 92 Beijing 100876 China Department of Electrical Engineering Northern Illinois University DeKalb IL 60115 National key laboratory of science and technology on blind signal processing Chengdu Sichuan 610041 China

ISBN: (纸本)9781785618154;9781785618161

In this paper, cluster centers are defined as local maxima in the density which is called density peaks. We use an algorithm based on density peaks to cluster multipath components (MPCs) whose parameters are estimated by the space-alternating generalized expectation-maximization (SAGE) algorithm. To validate its performance, we apply it to the data from a channel measurement in an indoor scenario at 28 GHz and compare it to the widely used KPowerMeans algorithm with the same distance metric of multiple component distance (MCD). Through the results, we find it can cluster MPCs more accurately validated by Calinski-Harabasz (CH) and Davies-Bouldin (DB) indices. More importantly, it overcomes the shortcoming of setting the number of clusters manually because we can determine the cluster centers automatically. Besides, the efficiency of DP clustering is far superior to KPowerMeans because what it needs is only a distance matrix and we only need to compute it once. At the end, statistical analysis of clusters are presented to provide insights in channel modeling of millimeter wave.

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