Deep learning models.
- algorithmSim.py: The main frame of the whole net, combining all the solitary modules. Multichannel Gain and the Obstruction Network are implemented directly in the main frame class Algorithm.
- Convolutional_RE_Net.py: Convolutional RE-Net is realized.
- weightGen.py: The two nonlinear models in the Obstruction Network and the RM-Net are realized here.
- Linear_RE_Net.py: Linear RE-Net is realized.
Some useful functions and settings.
- Config.py: Basic settings.
- dist.py: Calculate the log-distance.
- line_calc.py: Calculate the line equation.
- load_dict.py: Load the trained part into the combined framework.
- Map.py: Transform the location pair to a position map.
Generate the dataset.
- locPair.py: Obtain the location pair and the RSS measurements.
- training.py: Train our framework.
- weight_pretrain: Pre-train the nonlinear models in the Obstruction Network and the RM-Net.
- performance_evaluation.py: Evaluate the performance of our framework with whole datasets.
- Set the basic settings, like the dataset filename in training.py, all the parameters in the Config.py.
- Initialize the parameters of Multichannel Gain in algorithmSim.py.
- Run weight_pretrain.py.
- Run training.py.
- Run performance_evaluation.py to see the performance.