A joint centroid localization method based on error correction for wireless sensor network node localization
A wireless sensor and network node technology, which is applied in the direction of location information-based services, positioning, and radio wave measurement systems, can solve the problems of high power consumption of nodes, high computational complexity, and small positioning range, so as to improve positioning accuracy Effect
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specific Embodiment approach 1
[0061] Specific Embodiment 1: An error-correction-based joint centroid positioning method suitable for wireless sensor network node positioning described in this embodiment is implemented according to the following steps:
[0062] Step 1. Use the error probability distribution to realize high-precision positioning of some unknown nodes. The specific process is:
[0063] Step 1 (1): Calculate the distance between the unknown node and □ anchor nodes by receiving signal strength, where n represents the number of anchor nodes within the communication radius of the unknown node;
[0064] Step 1 (2): Solve the coordinates of the unknown nodes and derive the error function;
[0065] Step 1 (3): Obtain the error probability density function through measurement;
[0066] Step 1 (4): Use the function obtained in Step 1 (3) to calculate the joint probability distribution:
[0067] The distance from each anchor node to the unknown node is measured independently, and the joint probabilit...
specific Embodiment approach 2
[0094] Specific implementation mode 2: The difference between this implementation mode and specific implementation mode 1 is that the process of solving the coordinates of unknown nodes described in step 1 (2) is:
[0095] The distance between any two anchor nodes within the communication radius of the unknown node is calculated as follows:
[0096] m i = ( x i - x ) 2 + ( y i - ...
specific Embodiment approach 3
[0104] Specific implementation mode three: this implementation mode is different from specific implementation mode one or two in that: the process of deriving the error function described in step one (two) is:
[0105] Since the error is very small relative to the distance, the error of the square term of the error is smaller and can be ignored. From (18) and (19) formulas can be obtained
[0106] x = 1 4 [ ( d i 2 + 2 d i λ i + λ ...
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