Multi-target Tracking Algorithm Based on Rough and Precision Association Mixing FCM in WSN

Liu, Mei; Huang, Daoping; Gao, Huanping

doi:10.1109/cinc.2009.16

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Numerous research findings have been reported on these topics over the past two decades (for example, see the reviews in [1], [2]). Some publications are concerned with a specific application [3]- [9]; other papers have formulated and solved problems in the area of distributed estimation, detection and tracking [10]- [16]. In particular, an important research thrust in the field of distributed estimation is the design of practical distributed algorithms, choosing either to optimize a distributed sensor network with respect to energy consumption during transmission [17]- [19] or to impose bandwidth constraints and thus focus on designing an optimal quantization strategy for the distributed network [20]- [23].…”

mentioning

confidence: 99%

Distributed Estimation of a Parametric Field: Algorithms and Performance Analysis

Talarico

Schmid

Alkhweldi

et al. 2014

IEEE Trans. Signal Process.

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This paper presents a distributed estimator for a deterministic parametric physical field sensed by a homogeneous sensor network and develops a new transformed expression for the Cramer-Rao lower bound (CRLB) on the variance of distributed estimates. The proposed transformation reduces a multidimensional integral representation of the CRLB to an expression involving an infinite sum. Stochastic models used in this paper assume additive noise in both the observation and transmission channels. Two cases of data transmission are considered. The first case assumes a linear analog modulation of raw observations prior to their transmission to a fusion center. In the second case, each sensor quantizes its observation to M levels, and the quantized data are communicated to a fusion center. In both cases, parallel additive white Gaussian channels are assumed. The paper develops an iterative expectationmaximization (EM) algorithm to estimate unknown parameters of a parametric field, and its linearized version is adopted for numerical analysis. The performance of the developed numerical solution is compared to the performance of a simple iterative approach based on Newton's approximation. While the developed solution has a higher complexity than Newton's solution, it is more robust with respect to the choice of initial parameters and has a better estimation accuracy. Numerical examples are provided for the case of a field modeled as a Gaussian bell, and illustrate the advantages of using the transformed expression for the CRLB. However, the distributed estimator and the derived CRLB are general and can be applied to any parametric field. The dependence of the mean-square error (MSE) on the number of quantization levels, the number of sensors in the network and the SNR of the observation and transmission channels are analyzed. The variance of the estimates is compared to the derived CRLB.Index Terms-Wireless sensor network, EM algorithm, maximum-likelihood estimation, Cramer-Rao lower bound, distributed parameter estimation.

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confidence: 99%

Distributed Estimation of a Parametric Field: Algorithms and Performance Analysis

Talarico

Schmid

Alkhweldi

et al. 2014

IEEE Trans. Signal Process.

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“…Wang et al (2012) and Lin et al (2009) use Kalman filter to research on target tracking. Liu et al (2009) proposes a rough and precision association mixing FCM algorithm with Kalman filter for multi-target tracking. Its correct association rate of track increases to 98.3 from 86.7% compare with traditional method.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temperature and Humidity Data Fusion Algorithm Based on Kalman Filter

Chen¹,

Xu²,

Luo³

et al. 2013

RJASET

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In order to save system energy, enhance data-gathering accuracy and improve data-gathering efficiency in the temperature and humidity monitoring system based on wireless sensor networks, Multi-temperature and Humidity Data Fusion Algorithm based on Kalman Filter (MHDFA-KF) is proposed. In temperature and humidity sensor nodes, measured data are gathered and sent to sink node. In sink nodes, weighted fusion algorithm is used to fuse the received data and the fused data are sent to base station. In base station, Kalman filtering algorithm is used to filter the received data from sink nodes or sensor nodes. The time update equations and measurement update equations are used to iteratively calculate state variables and error covariance. Finally, the true value of temperature and humidity is obtained. The experimental results show that MHDFA-KF algorithm filters the data Gaussian noise, reduces the data measured error and obtain the true value. Under certain conditions, MHDFA-KF algorithm can be applied in temperature and humidity monitoring system based on wireless sensor networks. It has certain value.

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“…On the other hand, multiple-target tracking is widely encountered in many real applications and hence has been attracting the interests of many researchers. Some multiple-target tracking algorithms have been proposed [12], such as nearest neighbor (NN), probabilistic data association (PDA), the joint probabilistic data association (JPDA), multiple hypothesis tracking (MHT), Fuzzy Cluster Means In [13], a Markov chain Monte Carlo data association (MCMCDA) algorithm is presented, which solves the data association problems arising in multi-target tracking in a cluttered environment. A distributed data association algorithm for multi-target tracking is proposed in [14] by combining joint probabilistic data association and the Kalman filter based consensus algorithm.…”

Section: Introductionmentioning

confidence: 99%

Target tracking with size estimation in wireless sensor networks

Wang

Cheng

Chen

et al. 2011

2011 International Conference on Wireless Communications and Signal Processing (WCSP)

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The information of target size is useful for a lot of target tracking applications of wireless sensor networks, however, it has been neglected in most existing work. This paper considers the problem of target tracking with size estimation by ultrasonic sensors, which are able to measure the relative distance with satisfactory accuracy. We first propose a recursive algorithm for single target tracking and size estimation by adopting the Extended Kalman Filter (EKF) method. For the multiple-target case, a novel target identification algorithm, which aims to minimize the consecutive square estimate error, is given to track multiple targets and estimate the corresponding size simultaneously at each time step. Extensive simulations demonstrate the effectiveness of the proposed algorithms.

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Multi-target Tracking Algorithm Based on Rough and Precision Association Mixing FCM in WSN

Cited by 4 publications

References 7 publications

Distributed Estimation of a Parametric Field: Algorithms and Performance Analysis

Distributed Estimation of a Parametric Field: Algorithms and Performance Analysis

Multi-Temperature and Humidity Data Fusion Algorithm Based on Kalman Filter

Target tracking with size estimation in wireless sensor networks

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