## 1 Introduction  
Data fusion is a significant technique for detection, estimation and decision making. As Joint Directors of Laboratories (JDL) defines [1], data fusion is a \"multilevel, multifaceted process handling the automatic detection, association, correlation, estimation, and combination of data and information from several sources\". Hence, its objective is to make the optimal or sub-optimal use of the information generated by multiple knowledge sources and sensors. Now data fusion based systems are widely used in many areas, such as sensor networks, robotics, video and image processing, market planning, data mining, and knowledge discovery applications [2; 3; 4]. In the present literature, data fusion is usually categorized into three types: data-level, feature-level, and decision-level fusions [5; 6]. To take account of the situations constrained on the communication bandwidth or the data storage capacity, the data-level and feature-level fusions may not always be applicable, compared with the decision-level fusion [5]. That is, preliminary decisions should be made at each local detector in order to compress the bits transmitted to the decision fusion center. Then this center will make a global decision based on these local decisions and some fusion criterions. Due to the various types of detector (or classifier, sensor) outputs, decision-level fusion considered in the literature can be divided into three categories [7]: the abstract level, the rank level, and the measurement level. In [8], the authors have classified many typical decision-level fusion methods into these three categories. In detail, the Majority Voting [9], Weighted Majority Voting [10], Behavior Knowledge Space [11], and Naive-Bayes Combination [12] methods are belong to the first category; the Class Set Reduction methods (including intersection of neighborhoods and union of neighborhoods) and the Class Set Reordering methods (including highest rank method, Borda count method and logistic regression method) [13] belong to the second category; the Class-Conscious methods [14] and the Class-Indifferent methods (such as Decision Template combiner[15] and Dempster-shafer combiner [16]) belong to the last category. On the other hand, ultra-wideband (UWB) technology is attractive for its potential applications in Wireless Personal Area Networks (WPAN) [17; 18; 19]. It employs the short pulses (with bandwidths of several GHz) to transmit its information symbols at low power [20]. The advantages of UWB also stem from its ultra-wideband nature, such as good information hiding ability and less sensitivity to multipath fading [21; 22]. To reinforce its multiple-access (MA) ability in the multiuser occasion, UWB can be combined with traditional spread-spectrum (SS) techniques, which were firstly proposed by [PERSON] in [23], and with subsequent analyses in [24; 25; 26]. Among them, direct sequence UWB (DS-UWB) is an efficient MA scheme [21; 27; 28], where binary phase-shift keying (BPSK) can be employed and a large number of active users can share the same frequency bandwidth simultaneously, interfering with others [29]. (Please note that, another important spectrum sharing strategy is the cognitive radio technique. However, due to the space limitation, this technique will not be studied in this paper. The interested readers can refer to [30] for more information.) Therefore, the multiple access interference (MAI) exists and conduces to the performance aggravation of the conventional detector (CD) (also called the single-user matched filter). (For this reason, a lot of receiver solutions have been proposed in the literature. In [31], the multiple antenna diversity was employed in single-user UWB systems. In [32], a stop-and-go strategy based on energy detection in CD receiver was applied to alleviate excessive noise collection in single-user UWB clustered multipath channels. In [33], a zonal based Rake receiver was further proposed for multiuser UWB systems.)