kernel density is important

Regarding targeting being annoying in terms of evaluating the entire angular range, how are you doing that currently? Are you just calling a kernel density function on a large number of fixed points?

Here are three examples of ways to perhaps calculate kernel density faster in the context of targeting where you only care about the maximum:

1. If you take the derivative of your kernel density function, you should be able to find the zero-crossings of the slope, and only calculate the kernel density at those points.
2. One could also try approaches like skipping the kernel density calculation for angles which are too far from any data points.
3. Or maybe even use the data points themselves as the angles to run the kernel density calculation for.
4. With certain exceptionally simple kernel density functions (i.e. rectangle like I use in RougeDC/Scarlet's targeting), you can find the peak extremely fast with specialized algorithms also.

re #1: That seems to break for me, because (taking the Gaussian example) if I have two data points, centers -0.25 and 0.25 .. the maximum of the total area after calculating both kernels will be at x=0, which wasn't a zero-crossing of either Gaussian point in isolation.

re #2: I like this idea!

I've just now switched (experimentally) to using the Tricube kernel because I like it's shape: flattish in the center and trailing off to either side. I have it adjusted to slightly overhang the precise intersection width of each data point. Since it only exists from (-1,1), I've got some of your suggestion #2 built in, and turn skipping has pretty much ceased! We'll see how well this kernel compares, of course....