Optimal sensor placement for source tracking under synchronization offsets and sensor location errors with distance-dependent noises

Yang, Yang; Zheng, Jibin; Liu, Hongwei; Ho, K.C.; Chen, YangQuan; Yang, Zhiwei

doi:10.1016/j.sigpro.2021.108399

Cited by 12 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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. Some exceptions include the work of Yang et al [ 16 ], which considers TDoA measurements and presents a complete model of the noise depending on inter-distances, as well as a model of the synchronization offsets and the sensor location errors. In [ 6 , 19 ], the measurements are ranges, and the noise is a Gaussian process in which the variance depends on distance.…”

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 2 more Smart Citations

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

Villa

Ferreira

Cruz

2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Villa

Ferreira

Cruz

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…underwater structures, the influence of ultra-high artifacts, etc. Therefore, it is extremely important to study how to optimize the layout and the number of sensors [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Research on optimized layout of bridge sensors based on MAC stepwise addition and subtraction algorithm

Wu¹

2022

J. vibroeng.

View full text Add to dashboard Cite

The difference in the location and number of sensors induces the acquisition effect of structural state information in the bridge health monitoring system to a certain extent, which is also one of the most difficult problems restricting the development of bridge health monitoring technology. Based on the Modal Assurance Criterion, this paper took a cantilever simply supported beam as an example and adopted the Effective Independence method, Kinetic Energy method and QR decomposition method to optimize a sensor layout. Taking the number of sensors as 5, the layout and MAC matrix factors were analyzed by comparing the MAC matrix and its distribution diagrams obtained by different methods. The results showed that the stepwise subtraction method based on QR decomposition was the best. And by changing the number of sensor, it was found that the optimal number of sensor was 9. It was also found that the existing methods had the shortcomings of over-reliance on the selection of initial measuring points. In order to improve this defect, cyclic iteration of stepwise addition and subtraction was introduced, and an optimal layout method based on the stepwise addition and subtraction of the number of sensors was proposed. Through screening and optimization of this method, the average off-diagonal element in the calculation example was reduced from 0.118 to 0.006 to achieve the satisfactory optimization effect. At the same time, this method was employed to optimize the layout of a concrete continuous rigid-frame bridge with 9 and 19 sensors, and the results showed that the sensor layout was more uniform. Therefore, the proposed method provides the opportunity to obtain fair optimization results and to have a considerable engineering application value.

show abstract

“…In [15][16][17][18], the parameter identifiability of TD-based elliptic localization in MIMO systems was studied by comparing the number of measurement equations and that of unknowns. The optimal sensor placement, under the presence of sensor position perturbations, was designed by maximizing the determinant of the underlying FIM in [19][20][21]. Sensor position perturbations change TDs nonlinearly and, further, changes of TDs are equivalent to phase shifts varying with frequency.…”

Section: Introductionmentioning

confidence: 99%

Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks

2022

View full text Add to dashboard Cite

In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development.

show abstract

Optimal sensor placement for source tracking under synchronization offsets and sensor location errors with distance-dependent noises

Cited by 12 publications

References 29 publications

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

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

Research on optimized layout of bridge sensors based on MAC stepwise addition and subtraction algorithm

Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks

Contact Info

Product

Resources

About