Abstract: The nodes in the sensor network are vulnerable to interference when sensing data, so it gets the wrong data. In order to solve the problem of fault tolerance, a two value data-based fault tolerant multiple sources  localization algorithm（TMSFTL） is proposed. Firstly, the error probability of sensor nodes is analyzed, and the target source is identified and located by two valued data. When identifying, it uses Distributed Competitive Leader（DCL） algorithm to generate a leader node. By estimating the number of Leader nodes, it achieves the identification of the target source. Based on grid voting（GBV） mechanism, the target source is located in the positioning stage. In [Rc=15,][Rs=1.5] conditions, TMSFTL and DNLEP algorithm compare three aspects of the identification of two or more sources, the positioning performance, in the case of noise and error TMSFTL algorithm maintains high positioning performance, the error probability is 0.25, root mean square error is less than 8 m, the performance is much better than DNLEP algorithm.

Key words: error probability, identification, location, grid vote, two value data

摘要： 传感网中的节点在感测数据时易受到干扰，故获取到出错的数据。为了解决容错定位问题，提出基于二值数据的多目标容错定位算法（TMSFTL）。该算法首先分析传感节点的差错概率情况，利用二值数据对目标源进行识别及定位。在识别时采用分布式竞争领导者（DCL）算法产生领导者（leader）节点。通过估算Leader节点数以实现对目标源的识别。在定位阶段采用基于网格投票（GBV）机制对目标源进行定位。在[Rc=15]，[Rs=1.5]条件下TMSFTL与DNLEP算法在对两个或多个目标源的识别，定位性能三方面进行了对比，在噪声和差错情况下TMSFTL算法保持高的定位性能，在差错概率为0.25的环境，均方根误差小于8?m，其性能远优于DNLEP算法。

关键词: 差错概率, 识别, 定位, 网格投票, 二值数据