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The first example of tracking, is this the same thing as dead reckoning? I've always been confused on the term "tracking" since it is used a lot in common speec

by blharr 2y ago

The first example of tracking, is this the same thing as dead reckoning? I've always been confused on the term "tracking" since it is used a lot in common speech, but seems to mean some specific type of 'tracking'

defrost 2y ago
Dead reckoning is a form of prediction, based on past evidence that indicates location then, you are reckoning (best guessing) a current position and detrmining a direction to move forward to reach some target. "Past evidence that indicates" is deliberate phrasing, in the majority of these examples we are looking at acquired data with noise; errors, instrument noise, missing returns, etc. "Tracking" is multi-stage, there's a desired target to be found (or to be declared absent) in noisy data .. that's pattern search and locking, the trajectory (the track) of that target must be best guessed, and the best guess forward prediction can be used to assist the search for the target in a new position. This is not all that can be done with a Kalman filter but it's typical of a class of common applications.
hansvm 2y ago
Kind of. "Tracking", here, means providing some kind of `f(time) -> space` API. Dead reckoning is a mechanism for incorporating velocity and whatnot into a previously estimated position to estimate a new position (and is also one possible way to implement tracking, usually with compounding errors). The Kalman filter example is better than just dead reckoning. For a simple example, imagine you're standing still but don't know exactly where. You have an API (like GPS) that can estimate your current position within some tolerance. If you're able to query that API repeatedly and the errors aren't correlated, you can pinpoint your location much more precisely. Back to tracking with non-zero velocity, every new position estimate (e.g., from GPS) can be incorporated with all the information you've seen so far, adjusting your estimates of velocity, acceleration, and position and giving you a much more accurate current estimate but also better data for dead-reckoning estimates while you wait for your next external signal. The technique (Kalman Filter) is pretty general. It's just merging all your noisy sources of information according to some ruleset (real-world physics being a common ruleset). You can tack on all sorts of other interesting information, like nearby wifi signals or whatever, and even very noisy signals can aggregate to give precise results. Another application I threw it at once was estimating my true weight, glycogen reserves, ..., from a variety of noisy measurements. The sky's the limit. You just need multiple measurements and a rule for how they interact.
- jampekka 2y ago
  This is a very educational and intuitive way of putting it, but to nitpick the Kalman filter is a very special case of this (it assumes a linear ruleset and Gaussian uncertainties in the sensor readings). What you're describing is in general the Bayesian filter (or Bayesian smoothing if you don't have to give the result immediately).
  - hansvm 2y ago
    Yep, that's right. I thought about adding that detail but decided it might detract from the main points. Hopefully anyone interested also sees your comment.

by blharr 2y ago

defrost 2y ago

Dead reckoning is a form of prediction, based on past evidence that indicates location then, you are reckoning (best guessing) a current position and detrmining a direction to move forward to reach some target. "Past evidence that indicates" is deliberate phrasing, in the majority of these examples we are looking at acquired data with noise; errors, instrument noise, missing returns, etc. "Tracking" is multi-stage, there's a desired target to be found (or to be declared absent) in noisy data .. that's pattern search and locking, the trajectory (the track) of that target must be best guessed, and the best guess forward prediction can be used to assist the search for the target in a new position. This is not all that can be done with a Kalman filter but it's typical of a class of common applications.

hansvm 2y ago

Kind of. "Tracking", here, means providing some kind of `f(time) -> space` API. Dead reckoning is a mechanism for incorporating velocity and whatnot into a previously estimated position to estimate a new position (and is also one possible way to implement tracking, usually with compounding errors). The Kalman filter example is better than just dead reckoning. For a simple example, imagine you're standing still but don't know exactly where. You have an API (like GPS) that can estimate your current position within some tolerance. If you're able to query that API repeatedly and the errors aren't correlated, you can pinpoint your location much more precisely. Back to tracking with non-zero velocity, every new position estimate (e.g., from GPS) can be incorporated with all the information you've seen so far, adjusting your estimates of velocity, acceleration, and position and giving you a much more accurate current estimate but also better data for dead-reckoning estimates while you wait for your next external signal. The technique (Kalman Filter) is pretty general. It's just merging all your noisy sources of information according to some ruleset (real-world physics being a common ruleset). You can tack on all sorts of other interesting information, like nearby wifi signals or whatever, and even very noisy signals can aggregate to give precise results. Another application I threw it at once was estimating my true weight, glycogen reserves, ..., from a variety of noisy measurements. The sky's the limit. You just need multiple measurements and a rule for how they interact.

jampekka 2y ago
This is a very educational and intuitive way of putting it, but to nitpick the Kalman filter is a very special case of this (it assumes a linear ruleset and Gaussian uncertainties in the sensor readings). What you're describing is in general the Bayesian filter (or Bayesian smoothing if you don't have to give the result immediately).
- hansvm 2y ago
  Yep, that's right. I thought about adding that detail but decided it might detract from the main points. Hopefully anyone interested also sees your comment.