模拟退火 - 传感器网络中的传感器定位
你好 我在理解无线传感器网络中的定位传感器问题时遇到了一个小问题。基于那篇文章 http://citeseerx.ist .psu.edu/viewdoc/download?doi=10.1.1.110.2833&rep=rep1&type=pdf 我即将编写一个小模拟程序来解决传感器网络中的定位传感器问题。
优化问题如下：
假设我们有一组 m 个传感器（锚点 ndo），每个传感器的位置已知，还有 n 个传感器（非锚点 ndo）位置未知。每个节点都有能力测量自身与相邻节点之间的距离（测量结果会被噪声破坏）。
我的任务是：
通过有噪声的距离测量和锚节点的位置来估计位置未知的所有节点的位置。
在文章中（我在问题开始时提到的）也是一个我不理解的成本函数。我只是不知道锚节点的位置如何帮助我估计所有节点的位置。
我希望有人能理解我所写的内容:) 对不起我的英语
Hi
I have a litte problem with understanding problem of localization sensors in wireless sensor networks. Based on that article
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.2833&rep=rep1&type=pdf
I'm about to write a little simulation program which would solve the problem of localization sensor in sensor network.
The optimalization problem looks like that
Assume we have a set of m sensors (anchor ndoes), each with known position and n sensors (non-anchor ndoes) with unknown location. Every node have an ability to measure the distance between itself and neigbour nodes (the measurments are corrupted with noise).
My task is:
Having noisy distance measurements and position of anchor nodes estimate the location of all nodes withy unknown positions.
In the article(which I mentioned at the begining of my questions) is also a cost function which I don't understand. I just don't know how the position of anchor nodes helps me with estimation of location of all nodes.
I hope somebody will understand what I'm writting about :) Sorry for my English
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您首先猜测非锚节点的位置。当您知道该节点的位置时，您可以使用毕达哥拉斯定理计算从该节点到每个其他节点的距离，本文将其称为估计距离。您还可以使用无线电强度或其他方式测量嘈杂的距离。
您将这两个距离相减，求平方，然后将网络中可用的每个测量距离的值相加。这就是你的成本函数，你试图让它尽可能小。
模拟退火的用处显然是您一开始不会猜测非锚节点的正确位置。您向随机方向稍微移动您的猜测之一，看看成本函数是否变得更好。如果是，则保留新的猜测。否则，你再试一次。这样做足够长的时间，最终你的猜测就会非常接近实际位置。
You start out by guessing positions for the non-anchor nodes. When you have a position for that node, you can calculate the distance from it to each other node using the pythagorean theorem, which the paper calls estimated distance. You also have a noisy measured distance, using radio strength or whatever.
You subtract those two distances, square it, and sum that value for each measured distance available in your network. That's your cost function, which you're trying to get as small as possible.
Where simulated annealing comes in is obviously you're not going to guess the right position for the non-anchor nodes at first. You move one of your guesses slightly in a random direction and see if the cost function gets better or not. If it does, you keep the new guess. Otherwise, you try again. Do this long enough, and eventually your guesses are pretty close to the actual position.