【正文】 nformation or measurements gathered from all sensors does not seem to be attractive, or may be feasible, especially in a large and dense sensor field due to the demands of high munication cost, an appropriate solution is to divide the sensors into a number of smaller groups to operate on the tasks where each group has a local processing unit. The fusion and pression of locally processed data can save the energy used for the transmission of raw data to the base station or the end user. These groups of sensors are monly called clusters 14 and one sensor is selected to be a cluster head playing the role of a local processing unit. Generic energyefficient clustering protocols for decentralized processing in sensor work can be found in [6]. A target localization problem using clustering technique may require a somewhat more specific formulation. Since the ultimate goal is to identify a particular point which is the most likely target location, the critical information should be available in the corresponding target area. Hence, only the most informative cluster may be needed and that motivates the design of an efficient cluster formation at the particular time and place. Cluster forming protocols for the purpose of target localization and tracking have been presented in [7] where the Dynamic SpaceTime Clustering (DSTC) algorithm was proposed to work as a cluster forming protocol based on Closest Point of Approach (CPA). Information Driven Sensor Querying (IDSQ) introduced by Zhao et al [8] allows the maximum information gain for the dynamic clustering using an information utility measure. Dynamic clustering for acoustic target tracking was presented in [9]. A sparsely placed highcapability sensor scenario (expected to play cluster head roles) is assumed and a cluster is formed when the acoustic signal strength detected by the cluster head exceeds a predetermined threshold. The cluster’s priority is to integrate the measurements collected at each cluster member to represent the earning knowledge from each cluster. It is fairly application specific in order to describe the mechanism of the information management associated with the cluster formation. Generally, all members municate with the cluster head either by direct or multihop munication. The signal processing functions are carried out at the cluster head before transferring pressed data to the base station or end user. We will call this a centralized processing scheme. A specific application such as target localization using conventional methods, however, requires a circumspect design in order to obtain an accurate and costeffective system. The main reason is that the observation at each sensor is monly time series data. To individually transmit such data from all cluster members to be processed at cluster head entails a large amount of overall munication cost particularly when the cluster is designed to be large to reach the localization accuracy requirement. An alternative is to apply distributed processing by some means depending upon the characteristics of the utilized methods for a particular application. We exploit a wellknown scheme, the range difference based localization, for distributed processing in sensor works and demonstrate that the system performance can be improved when pared with the centralized method. Ⅲ . RANGE DIFFERENCE BASED LEAST SQUARE LOCALIZATION Range Differences (RD) can be derived from Time Difference of Arrival (TDOA) estimation through the relationship between distance and traveling speed of the signal over a medium. Time delay estimation technique [10] is the fundamental tool used to determine TDOAs. We will assume the existence of an optimal time delay estimator producing estimated TDOA perturbed by additive noise to model uncertainty. There have been a number of RDbased approaches proposed in the past 15 [11], [12]. We focus on a closedform least square method proposed in [11] since it was reported to be more efficient than the other schemes and was shown to approach the Cramer Rao Bound (CRB) in high Signal to Noise Ratio (SNR) environments. Let N sensors be assigned to participate in the localization process located at coordinates ? ? ? ?? ?NN yxyx ,....., 11 . Assuming the target is located at ? ?sss yxZ ,? , the differences of the distance between sensors i and j where i, j = 1, . . . , N and the source denoted by dij can be obtained by the basic relation: jiij DDd ?? where iD = ? ? ? ?22 isis yyxx ??? . RDs with respect to one arbitrary reference sensor are typically used. Without the loss of the generality, we select ? ?11,yx to be the location of reference sensor. The time series data collected from the other sensors together with the received signal at the reference sensor can produce the TDOA estimates and RDs can be derived from TDOAs using the knowledge of signal traveling speed. In the real application, however, the actual RDs are not available since there are some errors from TDOA estimation. Consequently, We have ?1id = 11 ii nd ? , i = 1. . . N. The TDOA estimate obtained by generalized cross correlation with Gaussian data is asymptotically normally distributed in high SNR environment [13]. Therefore, the RD estimate is also Gaussian and we assume 1in ～ N? ?21,0 i? . The Localization problem can be formulated as a linear least squares problem, bA ?? , where ???????????11222NNN dyxdyxA ??? ，???????????sssRyx? ，?????????????2122122121NN dRdRb ? ，? ? ? ?2121 yyxxR iii ???? These linear least square equations can be solved by a batch approach and the solution, ? ? bAAA TT 1?? ?? . However, we can update ?? without having to resolve the linear equations by a sequential least squares procedure [14] which can be described by letting ??nA = ? ? ? ??