&lt;title&gt;Improved data fusion through intelligent sensor management&lt;/title&gt;

Smith, Moira I.; Angell, Christopher R.; Hernandez, Marcel L.; Oxford, William

doi:10.1117/12.486928

Cited by 7 publications

(8 citation statements)

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“…2) Expected posterior entropy: the sensors that have the largest expected posterior entropies will be selected. 3) PCRLB calculated by EKF [7]. Figure 1 demonstrates the tracking scenario where true target trajectory and estimated trajectories by different sensor selection methods are compared.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It will be shown that in our method, even without the new measurements, we still can calculate the PCRLB driven by the particle filter, and estimate of the expected likelihood function is not required. The simulation results show that the particle filter PCRLB driven method outperforms the EKF posterior CRLB driven method [7] and the entropy based method [2]. The proposed method can be applied to either homogenous or heterogenous sensor networks with nonlinear models and non-Gaussian noise.…”

Section: Introductionmentioning

confidence: 98%

“…This lower bound gives an indication of performance limits, so it can be used as a criterion for sensor selection [7]. The PCRLB depends not only on the sensing accuracy of individual sensors, but also on sensor locations relative to target position and the posterior probability density function (PDF) of the target state.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Posterior Crlb Based Sensor Selection for Target Tracking in Sensor Networks

Zuo

Niu

Varshney

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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The objective in sensor collaboration for target tracking is to dynamically select a subset of sensors over time to optimize tracking performance in terms of mean square error (MSE). In this paper, we apply the Monte Carlo method to compute the expected posterior Cramér-Rao Lower Bound (CRLB) in a nonlinear, possibly non-Gaussian, dynamic system. The joint recursive one-step-ahead CRLB on the state vector is introduced as the criterion for sensor selection. The proposed approach is validated by simulation results. In the experiments, a particle filter is used to track a single target moving according to a white noise acceleration model through a two-dimensional field where bearing-only sensors are randomly distributed. Simulation results demonstrate the improved tracking performance of the proposed method compared to other existing methods in terms of tracking accuracy.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Posterior Crlb Based Sensor Selection for Target Tracking in Sensor Networks

Zuo

Niu

Varshney

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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“…Sensor resource management [2][3][4][5][6][7][8]: Approaches dealing with the dynamic activation of observation nodes to optimally schedule the sensing activities are referred to…”

Section: Introductionmentioning

confidence: 99%

Consensus-based distributed dynamic sensor selection in decentralised sensor networks using the posterior Cramér–Rao lower bound

Mohammadi

Asif

2015

Signal Processing

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“…The motivation for this work comes from sensor selection decisions [3][4][5][6][7][8][9][10][11][12][13], especially in geographically dispersed networks deploying an unrestrictedly large number of sensor nodes. Limitations in power, frequency, and bandwidth restrict the maximum number of active sensors that can simultaneously participate in the decentralized estimation process.…”

Section: Introductionmentioning

confidence: 99%

Full- and reduced-order distributed Bayesian estimation analytical performance bounds

Mohammadi¹,

Asif²

2014

IEEE Trans. Aerosp. Electron. Syst.

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Motivated by the resource management problem in nonlinear multisensor tracking networks, the paper derives online, distributed estimation algorithms for computing the posterior Cramér-Rao lower bound (PCRLB) for full-order and reduced-order distributed Bayesian estimators without requiring a fusion center and with nodal communications limited to local neighborhoods. For both cases, Riccati-type recursions are derived that sequentially determine the global Fisher information matrix (FIM) from localizedFIMs of the distributed estimators. We use particle filter realizations for these bounds and quantify their performance for data fusion problems through Monte Carlo simulations. Manuscript

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<title>Improved data fusion through intelligent sensor management</title>

Cited by 7 publications

References 0 publications

Posterior Crlb Based Sensor Selection for Target Tracking in Sensor Networks

Posterior Crlb Based Sensor Selection for Target Tracking in Sensor Networks

Consensus-based distributed dynamic sensor selection in decentralised sensor networks using the posterior Cramér–Rao lower bound

Full- and reduced-order distributed Bayesian estimation analytical performance bounds

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