scholarly journals GPS Sparse Multipath Signal Estimation Based on Compressive Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Huiping Qin ◽  
Xiaojuan Xie
Keyword(s):  
Compressive Sensing ◽  
Estimation Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Estimation Methods ◽  
Signal Estimation ◽  
Reconstruction Algorithms ◽  
Modified Newton Method ◽  
Reconstruction Performance ◽  
Multipath Signal

A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.

scholarly journals Estimating fusion properties for functionalised acids

2011 ◽  
Vol 11 (16) ◽  
pp. 8385-8394 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapour Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Organic Aerosol ◽  
Estimation Methods ◽  
Fusion Temperature ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation

Abstract. Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

scholarly journals A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 126 ◽  
Author(s):  
Bin Wang ◽  
Li Wang ◽  
Hao Yu ◽  
Fengming Xin
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Steepest Descent Method ◽  
Signal Recovery ◽  
Two Dimensional ◽  
Reconstruction Algorithms ◽  
Experimental Conditions ◽  
One Dimensional ◽  
Recovery Algorithm ◽  
Reconstruction Performance

The compressed sensing theory has been widely used in solving undetermined equations in various fields and has made remarkable achievements. The regularized smooth L0 (ReSL0) reconstruction algorithm adds an error regularization term to the smooth L0(SL0) algorithm, achieving the reconstruction of the signal well in the presence of noise. However, the ReSL0 reconstruction algorithm still has some flaws. It still chooses the original optimization method of SL0 and the Gauss approximation function, but this method has the problem of a sawtooth effect in the later optimization stage, and the convergence effect is not ideal. Therefore, we make two adjustments to the basis of the ReSL0 reconstruction algorithm: firstly, we introduce another CIPF function which has a better approximation effect than Gauss function; secondly, we combine the steepest descent method and Newton method in terms of the algorithm optimization. Then, a novel regularized recovery algorithm named combined regularized smooth L0 (CReSL0) is proposed. Under the same experimental conditions, the CReSL0 algorithm is compared with other popular reconstruction algorithms. Overall, the CReSL0 algorithm achieves excellent reconstruction performance in terms of the peak signal-to-noise ratio (PSNR) and run-time for both a one-dimensional Gauss signal and two-dimensional image reconstruction tasks.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals 1-Bit Compressive Data Gathering for Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jiping Xiong ◽  
Qinghua Tang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressive Sensing ◽  
State Of The Art ◽  
Data Gathering ◽  
Reconstruction Algorithm ◽  
Experimental Results ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Reconstruction Algorithms

Compressive sensing (CS) has been widely used in wireless sensor networks for the purpose of reducing the data gathering communication overhead in recent years. In this paper, we firstly apply 1-bit compressive sensing to wireless sensor networks to further reduce the communication overhead that each sensor needs to send. Furthermore, we propose a novel blind 1-bit CS reconstruction algorithm which outperforms other state-of-the-art blind 1-bit CS reconstruction algorithms under the settings of WSN. Experimental results on real sensor datasets demonstrate the efficiency of our method.

scholarly journals Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6558
Author(s):  
Yang Chen ◽  
Chengcheng Hong ◽  
Michael R. Pinsky ◽  
Ting Ma ◽  
Gilles Clermont
Keyword(s):  
Blood Loss ◽  
Early Stage ◽  
Estimation Error ◽  
Estimation Method ◽  
Vital Signs ◽  
Estimation Methods ◽  
Clinical Implementation ◽  
Arterial Blood ◽  
Blood Loss Volume ◽  
Estimation Models

Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.

scholarly journals Dual-Channel Reconstruction Network for Image Compressive Sensing

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2549 ◽  
Author(s):  
Zhongqiang Zhang ◽  
Dahua Gao ◽  
Xuemei Xie ◽  
Guangming Shi
Keyword(s):  
Compressive Sensing ◽  
Linear Mapping ◽  
Reconstructed Image ◽  
Reconstruction Algorithms ◽  
Dual Channel ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Reconstruction Quality ◽  
Under Sampling ◽  
Fully Connected

The existing compressive sensing (CS) reconstruction algorithms require enormous computation and reconstruction quality that is not satisfying. In this paper, we propose a novel Dual-Channel Reconstruction Network (DC-Net) module to build two CS reconstruction networks: the first one recovers an image from its traditional random under-sampling measurements (RDC-Net); the second one recovers an image from its CS measurements acquired by a fully connected measurement matrix (FDC-Net). Especially, the fully connected under-sampling method makes CS measurements represent original images more effectively. For the two proposed networks, we use a fully connected layer to recover a preliminary reconstructed image, which is a linear mapping from CS measurements to the preliminary reconstructed image. The DC-Net module is used to further improve the preliminary reconstructed image quality. In the DC-Net module, a residual block channel can improve reconstruction quality and dense block channel can expedite calculation, whose fusion can improve the reconstruction performance and reduce runtime simultaneously. Extensive experiments manifest that the two proposed networks outperform state-of-the-art CS reconstruction methods in PSNR and have excellent visual reconstruction effects.

scholarly journals A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3025 ◽  
Author(s):  
Weijian Si ◽  
Fuhong Zeng ◽  
Changbo Hou ◽  
Zhanli Peng
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sample Covariance Matrix ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Estimation Process ◽  
Sample Covariance ◽  
Coprime Arrays

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

scholarly journals Technical Note: Estimating fusion properties for polyacids

2011 ◽  
Vol 11 (3) ◽  
pp. 7535-7553 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapor Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Technical Note ◽  
Estimation Methods ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation ◽  
Liquid Vapor

Abstract. Organic aerosol (OA) components are generally assumed to be liquid-like. Hence, to describe the partitioning of these components, the liquid vapor pressure of these components is desired. Polyacids and functionalized polyacids can be a significant part of OA. But often, measurements are available only for solid state vapor pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapor pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or totally, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of polyacids. Next, we develop a simple estimation method, specifically for this kind of compounds, reducing significantly the estimation error.

A Two-Way Relay Channel Estimation Method Based on Sparsity Adaptive Matching Pursuit Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 1037-1042
Author(s):  
Ju Rong Wang ◽  
Jin He Zhou
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Prior Information ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Least Square ◽  
Estimation Methods ◽  
Relay Channel

To solve the problem that many existing two-way relay channel (TWRC) estimation methods require the sparse degree of the channel as prior information, we introduced a novel iterative greedy reconstruction algorithm based on compressed sensing (CS), called the sparisty adaptive matching pursuit (SAMP) to reconstruct the channel impulse response under orthogonal frequency division multiplexing (OFDM) system. The most innovative feature of SAMP is its capability of channel reconstruction without prior information of the sparse degree. Under the same condition we compared the algorithm with the other channel estimation methods including conventional least square (LS) algorithm, minimum mean square error (MMSE) algorithm and a orthogonal matching pursuit (OMP) algorithm based on CS. Simulation results show that the proposed algorithm has a better estimation performance and the algorithm improves the utilization of communication resources such as spectrum and energy. Thus it is suitable for real application.

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