Distributed compressive sensing in heterogeneous sensor network

Liang, Jing; Mao, Chengchen

doi:10.1016/j.sigpro.2015.10.026

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Patch-based image priors have drawn more and more attention from experts to try various regularization schemes. Especially formulating the distribution of image patches has proven to get a stable solution [11], which mainly use the non-local self-similarity [12], [13], fields of experts [14], learned patch distribution [15]- [18]. Cho et al [19] proposed a variational image restoration method that divided the image into square patches and used image priors to each patches independently.…”

Section: A Related Workmentioning

confidence: 99%

Patch-Based Nonlocal Adaptive Gradient Regularization for Image Restoration in Sensor Networks

Shi

2019

IEEE Access

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In this paper, we propose a novel patch-based adaptive nonlocal gradient regularization method for image restoration in sensor networks. It formulates the hyper-Laplacian distribution to regularize the global gradient distribution. The patch-based nonlocal gradient prior is utilized to regularize the nonlocal self-similarity of image gradients. Firstly, the L 0-norm smoothing scheme is used innovatively as the preprocessing step to preserve strong edges, which are critical to improve the accuracy of clustering the similar image patches. Then, adaptive weights for each patches are developed from a set of clustered nonlocal self-similarity patches by learning the the expectation and variance for sparse gradient distribution at each pixel. Comparing with several recent state-of-the-art methods, experimental results show that the proposed method has better performance in alleviating block effects and preserving image details. INDEX TERMS L 0-norm regularization, image restoration, hyper-Laplacian, image priors.

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Section: A Related Workmentioning

confidence: 99%

Patch-Based Nonlocal Adaptive Gradient Regularization for Image Restoration in Sensor Networks

Shi

2019

IEEE Access

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“…At present, threshold segmentation method [6][7][8][9][10], algorithm based on image processing [11], Gaussian mixture model [12], cluster analysis [13], and machine learning [14][15][16][17][18][19][20][21][22] have been used for convective target identification. Especially, the method based on machine learning is widely used in radar meteorology and hydrology research and in recent years, such as fuzzy logic, decision tree and neural network, etc.…”

Section: Introductionmentioning

confidence: 99%

Thunderstorm identification algorithm research based on simulated airborne weather radar reflectivity data

Wang

Liao

Li³

et al. 2020

J Wireless Com Network

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In the past few decades, radar reflectivity data have been widely used in thunderstorm identification research. Many thunderstorm identification algorithms for ground-based weather radar have been developed. But for airborne weather radar, due to the relative scarcity of data, the thunderstorm identification research is insufficient and there are still few effective identification methods. Airborne weather radar has the realization capability of close-range detection, but most existing airborne weather radars do not have scanning capability. This paper proposes an airborne weather radar volume scan mode, under which there is a total of 31 sector scans at 31 elevations in a volume scan. And a reflectivity data simulation model of the airborne weather radar is established based on this scan strategy, then the ground-based X-band radar reflectivity data are used as input to obtain the simulated X-band airborne radar reflectivity data. Moreover, this paper studies a thunderstorm identification algorithm for the X-band airborne radar with the proposed scan mode. An improved SCI (storm cell identification) algorithm is proposed on the basis of the traditional SCI algorithm which is applicable to S-band ground-based weather radar. The results of thunderstorm identification carried out on the simulated airborne radar data show that the algorithm can effectively identify the thunderstorm cells in the mature stage and the developing stage.

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“…And these studies have strict accuracy requirements for target detection and distance estimation, which may not be practical in real-world applications. Moreover, most previous models regard that sensors in MC-SSN have equal sensing and communication capabilities, which is not fully applicable to the multi-modal MC-SSN [22] nowadays.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Tree System for Target Tracking in Mission Critical Sensor Networks

2019

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The emergence of mobile sensors dramatically improves the availability of mission critical sensors and sensor networks (MC-SSN), enabling it to be utilized in more challenging tasks. However, current researches aimed to target tracking in mobile MC-SSN are very limited. This paper first proposes an adaptive fuzzy tree system (AFS) for target tracking in MC-SSN. To avoid oversimplifying the target mobile pattern, which is unrealistic in real tracking tasks, we introduce the target circle into the model considerations to add a bit of uncertainty about the target position. At each time step of the tracking process, strategy-selection layer will activate the pursuit strategy or the diffusion strategy for the specific situation, which makes the system more intelligent and can be applicable to various scenarios. The pursuit strategy is built by a two-layer fuzzy tree to select and mobilize sensors that have not detected target, while the diffusion strategy uses a fuzzy inference system to balance the density of detected sensors. And all the hyper-parameters are tuned by particle swarm optimization (PSO). We performed a large number of simulations with two target trajectories: line and irregular. The simulation results show that the AFS significantly outperforms the state-of-the-art algorithm. Moreover, it is highly robust to various target motion patterns, making it competent for a variety of real target tracking scenarios. INDEX TERMS MC-SSN, target tracking, particle swarm optimization, fuzzy tree, moving algorithm.

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Distributed compressive sensing in heterogeneous sensor network

Cited by 12 publications

References 14 publications

Patch-Based Nonlocal Adaptive Gradient Regularization for Image Restoration in Sensor Networks

Patch-Based Nonlocal Adaptive Gradient Regularization for Image Restoration in Sensor Networks

Thunderstorm identification algorithm research based on simulated airborne weather radar reflectivity data

Adaptive Fuzzy Tree System for Target Tracking in Mission Critical Sensor Networks

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