An alpha-beta filter is a way to combine information from two different sources to make estimates. It can help reduce the amount of 'noise' (unwanted variation) in the estimates. To explain this a little more simply, imagine you had two different groups of people (Group A and Group B) guessing how tall a tree is. Group A might give one number, and Group B might give another number. Since each group is only guessing and no one knows the exact answer, there could be some variation in the estimates. An alpha-beta filter can be used to combine the estimates from both groups and produce an estimate with less variability. It works by including some of the estimate from each group, but giving more of the estimate to the group that has generally been more accurate in the past. The combination of both estimates helps reduce the amount of 'noise' in the estimate, giving a better estimate of the tree's height.