Suppose I有3个传感器: 传感器1, 传感器2和。
我知道的唯一变量是:
Distance from sensor1 to origin is 36.05
Distance from sensor2 to origin is 62.00
Distance from sensor3 to origin is 63.19
Distance from sensor1 to sensor2 is 61.03
Distance from sensor1 to sensor3 is 90.07
Distance from sensor2 to sensor3 is 59.50
This is how it would look like if you had the positions:
我如何仅利用这些变量计算每一点的位置?
这不是家庭工作,只是好奇。
各位无法确切地找到这些要点的立场,因为任何围绕这些地点进行的轮换,以及不对称,仍然留下同样的距离。
Do you want a way to find all the possible results? Finding the points is pretty straightforward, but do you need the method to be robust on noise?
这一过程称为trilateration。 正如其他人所指出的,如果没有更多的信息,就不可能找到对传感器的绝对、明确的立场——在2D、3D4个非偶然、非宇宙的传感器中,你需要3个非偶然的、非宇宙的传感器,以解决所有轮换/反思的模糊性。
在无线感官网络地方化领域对这一问题进行了大量研究,涉及不完整、无天范围测量、通信不可靠以及资源极其紧张等问题,使这一问题变得令人感兴趣。
已知地点的固定点可作为系统增长的种子,或用于在所有节点确定后进行矫正改造。
如果没有更多的信息,问题就不可能解决。 如果你增加更多的信息和一些噪音,那是站不住脚的。 见。 找到最适合核心领域()交叉点的观点,讨论如何解决这类问题。
Look at these images.
而且
你们会看到三角可自由轮换(因此不存在“固定”位置),而且一般情况下不需要第三种传感器间距离,因为它是由另外两个距离确定的。
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