Detection Thresholds for Tracking in Clutter - A Connection Between Estimation and Signal Processing

Fortmann, T.; Bar‐Shalom, Yaakov; Scheffe, M.; Gelfand, S.B.

doi:10.21236/ada479689

Cited by 24 publications

(53 citation statements)

References 1 publication

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Order By: Relevance

“…Since the modified Riccati equation (MRE) can be used to predict the rms position (or other) errors of the system [1], [2], we also applied the MRE to more efficiently evaluate whether processing the worse sensor first leads to smaller rms errors for a wider range of system and scenario parameters. Multisensor extensions of the MRE [3] were used to predict the rms tracking performance of the dynamic target model of (1)-(2) with t = 1 (single target).…”

Section: Modified Riccati Equation Analysesmentioning

confidence: 99%

“…The q1 and q2 functions are defined in [1] and [2] and depend on the dimension of the system, P D ; , and V k , the volume of the validation region (gate) at time k.…”

Section: Modified Riccati Equation Analysesmentioning

confidence: 99%

“…Uncertainties in the target motion and in the measured values, usually modeled as additive random noise, lead to Manuscript received October 3, 1999 corresponding uncertainties in the target state. Additional uncertainty regarding the origin of the received data, which may or may not include measurements from the targets or random clutter (false alarms), leads to the problem of data association [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…In tracking applications, however, sensors are usually of unequal qualities, and tracking performance may be affected by the order of processingsensorinformation.Thus,weinvestigateheretheoptimalorderof processing sensor information (in terms of minimizing the rms position error) in sequential implementations of the MSPDA algorithm when two sensors of different qualities are used. The results of our work are applicable to problems such as tracking aircraft with radar measurements or tracking submarines with sonar measurements [1], [2]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The optimal order of processing sensor information in sequential multisensor fusion algorithms

Pao

Trailovic

2000

IEEE Trans. Automat. Contr.

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Section: Modified Riccati Equation Analysesmentioning

confidence: 99%

“…The q1 and q2 functions are defined in [1] and [2] and depend on the dimension of the system, P D ; , and V k , the volume of the validation region (gate) at time k.…”

Section: Modified Riccati Equation Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The optimal order of processing sensor information in sequential multisensor fusion algorithms

Pao

Trailovic

2000

IEEE Trans. Automat. Contr.

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“…It must be observed that the updating ofx s is performed much the same as Kalman filter, see Section II in Fortmann et al (1985), Rhodes (1971) and Prokhorov and Saul'ev (1979). The two key information-analytic variables are the information matrix and information state vector where the former is the inverse of the covariance matrix,…”

Section: Distributed Estimation In Dynamic Systemsmentioning

confidence: 99%

Distributed estimation for adaptive sensor selection in wireless sensor networks

Mahmoud¹,

Hamid²

2014

International Journal of General Systems

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Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs.Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.

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Efficient calculation of information reduction factor for tracking in clutter

Hill¹,

Tough²

2014

Electron. lett.

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A reformulation of the information reduction factor (IRF) for tracking in the presence of measurement uncertainty is presented. A simple integral representation is employed to derive an expression for the IRF that can be calculated without approximation in terms of one-dimensional integrals. This significantly reduces the computational requirements of the calculation and is relevant to the offline calculation of tracking performance and the selection of detection thresholds to optimise tracking accuracy. A comparison with Monte Carlo integration shows that the new technique is approximately 380 times faster.

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Detection Thresholds for Tracking in Clutter - A Connection Between Estimation and Signal Processing

Cited by 24 publications

References 1 publication

The optimal order of processing sensor information in sequential multisensor fusion algorithms

The optimal order of processing sensor information in sequential multisensor fusion algorithms

Distributed estimation for adaptive sensor selection in wireless sensor networks

Efficient calculation of information reduction factor for tracking in clutter

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