Design and Analysis of Distributed Radar Sensor Networks

Liang, Jing; Liang, Qilian

doi:10.1109/tpds.2011.45

Cited by 90 publications

(37 citation statements)

References 28 publications

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“…In order to compare the satisfaction rate of algorithms clearly, we assume that the Demand matrix is fixed as [4, 2, 1, 4, 3, 5, 6, 3, 1, 7, 2, 8, 4, 1 ,6, 4, 4, 7, 6, 4] and frequency matrix is generated from [1,3] randomly, which is shown in Figure 6. The result shows that satisfaction rate of the improved parallel algorithm tends to 1.…”

Section: Results and Analysismentioning

confidence: 99%

“…domly. Di ∈ [0, α Ti], in which α is defined as the requirement coefficient and α ∈ [1,3]. The parameters are shown in Table 1.…”

Section: Simulation Conditionsmentioning

confidence: 99%

“…Since spectrum was a free cost to the designer there were few incentives to minimize spectrum footprint. However, with the growing interests on radar networks [2,3], spectrum allocation in radar network needs to be explored. However, the fixed spectrum management which allocates certain spectrum to the user leads to the low spectrum utilization rate.…”

Section: Introductionmentioning

confidence: 99%

“…A parallel spectrum allocation algorithm distributes many spectrums in single distribution cycle improving the spectrum efficiency. Literature [2] and [3] refer to the parallel algorithm as well. In order to get higher channel multiplexing rate, the literature [6] improved the parallel algorithm which make other users share the same channel with the user that have the largest label.…”

Section: Introductionmentioning

confidence: 99%

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Research on an improved parallel algorithm based on user’s requirement in cognitive radar network

Yan

Wang

Bao

2015

J Wireless Com Network

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In cognitive radar network, a parallel algorithm can allocate idle spectrum to cognitive radars, but it often ignores the user's requirement. According to the graph theory, the topology of the network is divided into two different groups. In the process of distributing spectrum, each group summarizes the distributed information after completing a distribution. When a user's demand is satisfied, every group is informed to not allocate the spectrum for the user anymore and deletes it from the topology at the same time. As a result, by deleting the node which the user's requirement has already been met, the other nodes which are interfered with the node on the same channel can participate into spectrum allocation. The improved algorithm can ensure high spectrum utilization taking a little time that is spent on the spectrum allocation. The simulation result shows that the spectrum utilization of the improved algorithm is higher than that of the traditional parallel algorithm, and the user's satisfaction increases greatly. Therefore, this paper aims to attempt to explore an improved parallel algorithm to resolve the problem by considering user's requirement.

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Section: Results and Analysismentioning

confidence: 99%

“…domly. Di ∈ [0, α Ti], in which α is defined as the requirement coefficient and α ∈ [1,3]. The parameters are shown in Table 1.…”

Section: Simulation Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on an improved parallel algorithm based on user’s requirement in cognitive radar network

Yan

Wang

Bao

2015

J Wireless Com Network

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show abstract

“…So a lot of methods on suboptimal approximate filtering are proposed [9]. Nonlinear filtering methods can be classified into five types [10,11]: 1) extended Kalman filtering (EKF), 2) interpolation filtering, 3) unscented Kalman filtering (UKF), 4) particle filtering, and 5) neural network filtering.…”

Section: Introduction Of Nonlinear Filteringmentioning

confidence: 99%

Evaluation of nonlinear filtering for radar data tracking

Liu

Wang

Sun

et al. 2015

J Wireless Com Network

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Radar tracking plays an important role in the area of early warning and detection system, whose precision is closely connected with filtering algorithm. With the development of noise jamming technology in radar echo signal, linear filtering becomes more and more difficult to satisfy the demands of radar tracking, while nonlinear filtering can solve problems such as non-Gaussian noises. There exist a lot of nonlinear filtering algorithms at present, owning their particular characteristics. With this in mind, we provide a comprehensive overview of different nonlinear filtering algorithms in radar tracking, including basic ideas and concrete steps of them. For a more clear presentation, we also make comparisons of them from all sides. Through the analyses of different nonlinear data filters, we find that the unscented Kalman data filter (UKF) can achieve better performance than others. Therefore, we will simulate and show the performance of UKF, and performance of the extended Kalman data filter (EKF) under the same condition will be taken as comparison, whose accuracy was not ideal for radar tracking data filtering.

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Perceptive Mobile Network (PMN)

2022

Joint Communications and Sensing

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Design and Analysis of Distributed Radar Sensor Networks

Cited by 90 publications

References 28 publications

Research on an improved parallel algorithm based on user’s requirement in cognitive radar network

Research on an improved parallel algorithm based on user’s requirement in cognitive radar network

Evaluation of nonlinear filtering for radar data tracking

Perceptive Mobile Network (PMN)

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