KNN-complexity-reduced-method

  基于LANDMARC的定位系统上进行的算法复杂度的减小的优化，包括了具体的优化后系统的实现，误差前后对比，改文章还提出了一种adaptive的定位算法，更利于外部变化环境下(In wireless networks, a client’s locations can be estimated using signal strength received from signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when signal-strength values change over time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as Location Estimation using Model Trees (LEMT), to reconstruct a radio map by using real-time signal-strength readings received at the reference points. This algorithm can take real-time signal-strength values at each time point into account and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodat)