外文翻译---传感器网中络基于分布范围差异的目标定位(编辑修改稿)

外文翻译---传感器网中络基于分布范围差异的目标定位(编辑修改稿)内容摘要：

ion: A practical linearcorrection leastsquares approach,” IEEE Trans. Speech and Audio Processing, vol. 9, no. 8, Nov 2020. [12] Y. T. Chan and K. C. Ho, “A simple and efficient estimator for hyperbolic location,” IEEE Transactions on Signal Processing, 1994. [13] G. C. Carter, “Time delay estimation for passive sonar signal processing,”IEEE Transactions 11 on Acoustics, Speech, and Signal Processing,1981. [14] S. M. Kay, Fundamentals of Statistical Signal Processing. Prentice Hall, 1993. [15] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms. McGrawHill, 2020. 12 Distributed Range Difference Based TargetLocalization in Sensor Network Chartchai Meesookho and Shrikanth Narayanan Department of Electrical Engineering Viterbi School of Engineering University of Southern California Email: , Abstract Target localization is a key application in the sensor work context. Of the various conventional methods can be applied, and have been proposed, the Range Difference (RD) Based method is attractive due to improved accuracy and ease of implementation it affords. While the basic concepts of the RD based method can be adopted to the case of sensor works, the data acquisition and aggregation procedures need to be formulated and characterized subject to the energy constraint. The challenge is to design an efficient algorithm that is economical and still accurate. In this paper, based on range difference localization method, we propose a distributed algorithm which allows the time delay estimation to be carried out at each participating sensor. The acquired data is fused using a sequential least squares scheme which enables the appropriate sensor selection based on the current estimate. The results, evaluated using realistic simulation models, illustrate that the distributed localization produces smaller error and consumes less energy than the centralized method. The advantage of distributed localization in terms of the accuracy bees more conspicuous when the number of participating sensors is small while the energy saving increases when the number of participating sensors is large. The proposed method accuracy is also more robust to decreasing target signal energy and the instantaneous error from the sequence of estimates can be approximated and used to reconcile the cost and the system performance. Ⅰ .INTRODUCTION Target localization is one of the key motivating applications for implementing sensor works. A large number of sensors enable the redundancy of the observations and close proximity to the target, and thus, improving the chances for improved target localization and tracking performance. Some example applications include localizing military vehicles in a battlefield and tracking wild animals in their natural habitat. Recently, conventional target localization methods have been applied to the case of sensor works. The Range Difference (RD) based method is particularly attractive in this context [1], [2] since it offers better ease of implementation than the Maximum Likelihood (ML) estimator [3], [4], is more accurate than energy based localization [5], 13 and does not require the prior knowledge of the signal generated by the target. While the basic concepts of the RD based method can be adopted to the sensor works problem, the data aggregation procedure needs to be developed and characterized. In traditional systems such as radars and microphone arrays, time series data collected from each sensor, the fundamental information needed in the process, is assumed to be available at the central processing unit without the concern for the cost incurred in gathering such information. However, due to the characteristics of sensor works, which are typically batterypowered and wireless, the energy expenditure for time series data exchange between sensors should be taken into account. The challenge is to design an efficient algorithm that is economical and still accurate. In [1], localization was implemented on a sensor array testbed but the munication cost was not considered. The clusterbased architecture for acoustic target tracking was studied in [2]. Noheless, the system performance subject to the munication protocol within the cluster was not addressed. We believe that the impact of the designed algorithm on the system efficiency is highly dependent on what specific method is implemented. In this paper, based on range difference localization, we propose a distributed algorithm which allows time delay estimation to be carried out at each participating sensor so that the amount of energy incurred for time series data transmission can be decreased. The acquired data, which are the range differences, are fused using a sequential least squares scheme. The sequential nature allows for efficient sensor selection based on the current estimate at each time step, thus, enabling accuracy to be improved. The results, evaluated using realistic models and conditions, illustrate that the distributed localization produces smaller error and consumes less energy than the centralized method. Notably, the advantage of distributed localization in terms of the accuracy bees more significant when the number of participating sensors is small while the energy saving increases when the number of participating sensors is large. The proposed method is also robust in that its accuracy is less affected by a target signal with low energy (lower SNR) and the instantaneous error from the sequence of estimates can be approximated and used to reconcile the cost and the system performance. Ⅱ .CLUSTERING FOR TARGET LOCALIZATION Since a centralized global processing of information or measurements gathered from all sensors does not seem to be attractive, or may be feasible, especially in a large and dense sensor fiel。