- freely available
Sensors 2010, 10(4), 2957-2974; doi:10.3390/s100402957
2. Indoor Positioning Algorithms
2.1. The Nearest Neighbor Algorithm
2.2. The K Weighted Nearest Neighbors Algorithm
2.3. The Probabilistic Approach
2.4. The Particle Filter
- Step 1: In the initial stage, N random sampling points (particles) is generated at the initial time as , and each particle is associated with the same weight. This paper generates 1000 particles with the equal weights uniformly distributed in the map, as shown in Figure 5.
- Step 2: The evolution of the system states by drawing the from , if the system model can be expressed as Xk = f (Xk−1, vk−1), then . To simplify the estimation, the evolution of the system states through the system model is illustrated as Equation (12).
- Step 3: Implement the process of weight update by Equation (11) when the current measurements are available, and assume that the distribution of the likelihood function is a Gaussian distribution. The is 1/N at each time step. Then the weight update is calculated by Equation (13):
- Step 4: To avoid the degeneracy, the resampling process is applied by generating a new set of with probability of .
- Step 5: The states estimation at time k is the expectation value of the regenerated particles in Step 4 (i.e., ).
3. Geographic Information System
4. Experiment Results and Analyses
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|KWNN + SIR particle filter (cm)||24.22||33.30||22.69||26.53|
|Kernel method (cm)||64.46||63.97||63.89||63.89|
|Kernel method + SIR particle filter (cm)||32.89||32.18||33.99||29.68|
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