I would like to be able to regenerate the heatmap in the Kraken app on Matlab. I am able to plot the GPS locations of the sdr and their respective DOA lines. However, I am unsure how to implement the heatmap. I have tried to initialize a grid over the map and then if the DOA line goes through each grid, I increase the value of each intersected square multiplied by an RSSI multiplier. However, my resulting heatmap does not look like the heatmap generated by the app. Is there a different approach to this that uses Confidence instead of RSSI?
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In the app the heatmap uses the full 360 degrees of DoA lobe data provided by the Kraken. So you need to map on the grid the DoA line 360 times for each datapoint, and increase each cell by the DOA lobe ‘power’ value.
We don’t use the RSSI in the heatmap algorithm, it’s irrelevant to this method. We also don’t use confidence. Mapping the full 360 degrees of data incorporates the natural confidence of the bearing reading into the grid more naturally.
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Yes, as I can understand RSSI is not used. However, how do you decide about the (lat, lon)? Do you have more than one kraken device deployed?
Can you elaborate on what you mean about deciding on the lat/lon?
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I see in @thackman27 heatmap that he is trying to estimate lat and lon and I was asking what procedure follows
I appreciate the help. I reworked my code and at each data point, I took the 360 DoA lines and turned them into scatter lines. Then I was able to determine if the points fell within the bounds of the grid squares of the heatmap and add to them accordingly. I was able to get a much more accurate heatmap. However, the intensity seems to be greater on the path of the vehicle.
You will need to ignore adding to the GPS location grid cell, otherwise that cell will always maximise since it’s adding on every value.
I would also test first by just activating the cells on the max DOA line for a few data points. Then you can tell if the grid algorithm you’ve written is activating the correct cells.
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