Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

Zaeemzadeh, Alireza; Joneidi, Mohsen; Shahrasbi, Behzad; Rahnavard, Nazanin

doi:10.1109/mass.2017.50

Cited by 19 publications

(17 citation statements)

References 24 publications

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“…These techniques such as LS estimator, convex optimization, and ML estimator are combined with the TDOA localization scheme. The combined TDOA localization technique with the optimization algorithms is a semi-definite programming (SDP) in [23,24], TDOA-LS based in [25,26], and TDOA-ML acoustic-based localization in [27]. The SDP-based method relaxes the original non-convex problem onto a convex set, thus effectively solving the new optimization problem [28].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, for a good estimation accuracy, the relaxation needs to be very tight, which is rather challenging, not to mention the high computational complexity for solving the final problem. LS method is a very useful method used to solve TDOA positioning problem and in [25], the squared-range based LS formulation is exploited. Then the problem is converted into a known class of optimization problems, namely generalized trust region sub-problem (GTRS), using the concepts in robust statistics.…”

Section: Related Workmentioning

confidence: 99%

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Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sun

Esmaiel

2020

Sensors

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Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sun

Esmaiel

2020

Sensors

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“…Slant range, azimuth and computed elevation angle of respective contributing radar are transformed to ECEF frame by using the Eqns. (5) or (6). Then the target position from ECEF to geodetic frame is computed using method provided in 26 .…”

Section: Coordinate Conversion From Aer To Geodetic Framementioning

confidence: 99%

3D Localisation of Target using Elevation Angle Algorithm with the use of Ground Radars

Raju¹,

Kashyap²

2020

Def. Sc. Jl.

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A new novel method based on elevation angle algorithm (EAA) is proposed in this paper, to obtain 3D position of target using range and azimuth measurements of two ground 2D radars. The EAA estimates optimal target elevation angle wrt contributing radar by solving a non-linear optimisation problem using Levenberg-Marquardt method in geo-centric frame such as earth-centred-earth-fixed. The target position in geodetic frame (WGS84) is then obtained using slant range, azimuth and estimated elevation angle. The proposed method is evaluated using simulated but realistic radar data and accuracy of estimated position is found to be comparable with true position (error within acceptable limit). The method is also evaluated with real data from actual ground 2D radars and estimated target position is found to be comparable with reference navigation data (GPS) on-board of target. For each radar, corresponding Extended Kalman filter (EKF) is used to handle noisy, asynchronous measurements and to provide estimated range and azimuth at common reference time for altitude estimation using proposed EAA method. In case of real data, the estimated altitude is found to be comparable GPS altitude with error less than 5 % of true altitude. From the study, it is found that EAA is suitable to estimate target position using measurements from only two contributing asynchronous 2D radars in real-time as compared to some other techniques such triangulation and Trilateration where at-least three radars are required to get the position of target. This method can be useful to utilise network of vintage long range 2D radars to determine target position and to fill the gap wherever/whenever target is out of detection range of 3D radars. In addition, EAA method is compared with commonly used methodology such range only localisation and results are presented.

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“…However, it should be emphasized that outlier measurements may occur frequently and inevitably because of the multipath effect, unintentional or malicious attack, and signal shadowing caused by waves [10,26,27]. To the best of our knowledge, only a few papers took outlier measurements into consideration when it came to localization in OSNs.…”

Section: Introductionmentioning

confidence: 99%

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

Mei

Xian

et al. 2019

Sensors

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As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer–Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

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Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

Cited by 19 publications

References 24 publications

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

3D Localisation of Target using Elevation Angle Algorithm with the use of Ground Radars

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

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