In this paper we consider indoor location based on WiFi signals under the condition that the location provider does not know the physical location of the access points. This scenario is different from most previous researches on indoor location. We proposed several algorithms for this purpose including set calibration data, calculate position and track moving rout. Results from some experiments show that our scheme is useful for many kind buildings such as Malls and office buildings.

This work presents a novel index structure, MHR-tree, for efficiently answering approximate string match queries in large spatial databases. The MHR-tree is based on the R-tree augmented with the min-wise signature and the linear hashing technique. The min-wise signature for an index node u keeps a concise representation of the union of q-grams from strings under the sub-tree of u. We analyze the pruning functionality of such signatures based on set resemblance between the query string and the q-grams from the sub-trees of index nodes. MHR-tree supports a wide range of query predicates efficiently, including range and nearest neighbor queries. We also discuss how to estimate range query selectivity accurately. We present a novel adaptive algorithm for finding balanced partitions using both the spatial and string information stored in the tree. Extensive experiments on large real data sets demonstrate the efficiency and effectiveness of our approach.