The Dependence of Aggregation in the Determination of Optimal Sensor Configurations for Source Localization

Costa, Russell

doi:10.1109/taes.2021.3060788

Cited by 1 publication

(4 citation statements)

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“…To consider cases in which the target performed a path, the metrics had to be aggregated over all possible target positions. The choice of the aggregation function defines a unique objective to be optimized [ 5 ]. Following the formulation presented in [ 5 ], consider

as n arbitrary positive numbers, then a generalized mean can be defined as:

where r controls the type of aggregation function.…”

Section: Measurement Model and Problem Descriptionmentioning

confidence: 99%

“…The choice of the aggregation function defines a unique objective to be optimized [ 5 ]. Following the formulation presented in [ 5 ], consider

as n arbitrary positive numbers, then a generalized mean can be defined as:

where r controls the type of aggregation function. Common functions are the min, max, geometric, harmonic, and arithmetic mean, corresponding to

, and

, respectively.…”

Section: Measurement Model and Problem Descriptionmentioning

confidence: 99%

“…The literature has extensively analyzed the problem of optimal sensor placement for source localization. Regarding the measurements between sensors and source, most works have considered direct range [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ], time of arrival [ 2 , 5 , 9 ], difference time of arrival [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], angle of arrival [ 2 , 5 , 17 ], or received signal strength [ 18 ]. However, although in various applications the measurement noises usually varies with distance, especially in underwater scenarios [ 19 ], the most common assumption is to consider noise to be independent of the distance and equal to all the sensors.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, it is possible to derive scalars from the FIM that can be used to evaluate the performance of a particular solution. The most common criteria for optimality is the determinant of the FIM [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 10 , 11 , 12 , 14 , 16 , 17 , 18 ], although the trace of the inverse of the FIM [ 9 , 15 , 17 ], the eigenvalues of the inverse of the FIM or a combination of them are also found in the literature [ 20 ]. In the optimal experimental design theory, the strategies that optimize based on these three criteria are classified as D-optimality, A-optimality, and E-optimality, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises

Villa

Ferreira

Cruz

2022

Sensors

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In source localization problems, the relative geometry between sensors and source will influence the localization performance. The optimum configuration of sensors depends on the measurements used for the source location estimation, how these measurements are affected by noise, the positions of the source, and the criteria used to evaluate the localization performance. This paper addresses the problem of optimum sensor placement in a plane for the localization of an underwater vehicle moving in 3D. We consider sets of sensors that measure the distance to the vehicle and model the measurement noises with distance dependent covariances. We develop a genetic algorithm and analyze both single and multi-objective problems. In the former, we consider as the evaluation metric the arithmetic average along the vehicle trajectory of the maximum eigenvalue of the inverse of the Fisher information matrix. In the latter, we estimate the Pareto front of pairs of common criteria based on the Fisher information matrix and analyze the evolution of the sensor positioning for the different criteria. To validate the algorithm, we initially compare results with a case with a known optimal solution and constant measurement covariances, obtaining deviations from the optimal less than 0.1%. Posterior, we present results for an underwater vehicle performing a lawn-mower maneuver and a spiral descent maneuver. We also present results restricting the allowed positions for the sensors.

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as n arbitrary positive numbers, then a generalized mean can be defined as:

where r controls the type of aggregation function.…”

Section: Measurement Model and Problem Descriptionmentioning

confidence: 99%

“…The choice of the aggregation function defines a unique objective to be optimized [ 5 ]. Following the formulation presented in [ 5 ], consider

as n arbitrary positive numbers, then a generalized mean can be defined as:

where r controls the type of aggregation function. Common functions are the min, max, geometric, harmonic, and arithmetic mean, corresponding to

, and

, respectively.…”

Section: Measurement Model and Problem Descriptionmentioning

confidence: 99%