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.

For #1 I did not mean the zero-crossing of any one point, I meant the zero-crossings of the sum of all the derivatives of the kernel density function. Of course, whether it's efficient to calculate those zeros or not all depends on what the kernel density function is (probably not practical for gaussian, trivial for triangular, as two extereme cases)

Hmm... tricube sounds like an interesting one, though that's quite a bit of multiplication it uses. I wonder if this is the sort of thing that would be worth doing a rough approximation of really. I mean... it probably wouldn't affect the results too much to do the kernel density as a piecewise "sum of rectangles" approximation, and it would be much faster.