## 1 Introduction  
An unmanned lunar rover will be part of the essential equipment for moon exploration in the second phase of the Chinese \"Chang'e\" mission [1,2]. The long-distance localization and navigation of a lunar rover cannot depend on lasers, which can not be used with obstacles. It cannot depend on a Global Positioning System (GPS) either, because there is no GPS on the moon. Other localization and navigation methods, such as inertia navigation, have large error accumulations, which cannot be used in long-distance navigation. Wireless sensor network technologies [3] can be used for the lunar rover's localization and navigation. Wireless sensor nodes are slung around with the movement of lunar rover. With the increase of detection area on the moon surface, a large number of sensor nodes need to be deployed, which constitute a large scale wireless sensor network. Therefore, there is an urgent need to develop a low complexity and high energy efficient localization algorithm for large scale sensor networks on the moon. Besides this, localization is also important and challenging in wireless sensor networks. A wireless sensor network comprises a mass of geographically separated sensor nodes [4]. Each sensor node consists of a microcontroller, a wireless transceiver battery and many different sensors. Locations of sensor nodes are necessary in a variety of applications, including environment monitoring, object detection, target tracking [5-7], security surveillance [8]. In addition, location information is also necessary for geographic routing protocols [9,10], clustering algorithm [11,12] and geographic data fusing algorithms [13-15]. From the point of view of localization accuracy, localization algorithms can be classified into two different types: range-based algorithms and range-free algorithms [16]. In range-free algorithms, the hop-count based scheme is simple and scalable. In this scheme, the distance between two nodes can be represented by the hop number between them. The classic hop-based algorithms are DV-Hop [17] and Hop-TERRAIN [18] algorithms. The location estimation accuracy is influenced largely by the distribution and density of nodes in the network. E.g., in the network, if the nodes in one region are dense, in another region are sparse, the same hop count in these different regions will represent different distances; if the whole network is sparse, the distance will be represented by a small hop count, which will be resulted in a large quantizing error. DHL [19] uses different hop distances for different nodes densities and uses neighbor numbers of nodes for their densities. In [20], intelligent robots are adopted to improve the node density. Those robots move to the static nodes with few neighbor nodes to enhance their density. The APIT algorithm [16] is another range-free area-based algorithm. The blind node is covered in many triangles formed by a set of anchor nodes. The center of these triangles is defined as the position of the blind node. The localization accuracy is improved by this method. The COCB-based algorithm [21] is also an area-based distributed scheme. It has lighter computational load and a lower energy consumption comparing with APIT. The CAB algorithm [22] does not require communication between neighbor nodes. The anchor nodes can transmit information at different power levels, dividing the radio range into a circle and rings. The blind node located in these rings uses their center as its position. These range-free algorithms can not determine the exact distance between neighbor nodes; therefore they can not obtain a high accurate localization. Range-based algorithms utilize relative accurate distance information between neighbor nodes for their localization. The distance information can be obtained (or calculated) by signal time of arrival (ToA), time difference of arrival (TDoA), angle of arrival (AoA), received signal strength indicator (RSSI) and so on [23]. The RSSI scheme is not very accurate due to non-uniform signal propagation.