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A very simple dead-reckoning navigation system can be designed by inte gration of xo = f (xo , y) for t to from some initial xo (to ) where xo = [no , eo , o ] The resulting dead-reckoning system state, xo (t) for t 0, will be used as the reference trajectory for the linearization process The navigation system equation can be simpli ed as follows: xo = f (xo , y) u cos( o ) eu cos( o ) (u + eu ) cos( o ) = (u + eu ) sin( o ) = u sin( o ) + eu sin( o ) ( + e ) e eu cos( o ) = f (xo , u) + eu sin( o ) (337) e

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The rst order (ie, linearized) dynamics of the error between the actual and navigation states are 0 0 sin( o ) u cos( o ) 0 eu u cos( o ) v sin( o ) 0 v= 0 0 (338) e 0 0 0 0 1

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where u = y1 and v = [ n, e, ] = x xo To derive this linearized model, the function f in eqn (336) is expanded using Taylor series Then eqn (337) is subtracted from the Taylor series expansion of eqn (336) to yield eqn (338) For additional discussion related to this example, see 9 Error state dynamic equations enable quantitative analysis of the error state itself Later, the linearized error state equations will be used in the design of Kalman lters to estimate the error state Prior to that, we must discuss such issues as stochastic modeling of sensor errors which is done in 4

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up as a cowboy in Montana riding rodeo and playing football, I d push all the chips forward It was very macho That s great when you re right, like the time I turned $10,000 into $1 million at a realtime trading championship Now, he says, his position size is less aggressive but he sleeps a lot better And I don t have margin calls anymore, he quips, only half-jokingly Williams learned a lot of lessons about the market by observing what happens in Las Vegas The whole adage in Vegas is that to win big, you have to bet big, he says They tell you that to separate you from your money Therefore, if you bet big in the stock market, that losing trade is going to come and you will lose your house If you bet big, you will blow up at some point So you never bet big To win big in the stock market, he says, you have to bet small I always bet small Even if everything lines up and Williams sees a perfect chart pattern, he won t bet beyond his traditional trade size I can be wrong, he explains I can be wrong three or four times in a row In fact, he says that accepting losses is one of the key attributes of a successful trader When you trade, you know you are going to be wrong People who can t accept being wrong are going to blow up their accounts If you trade often enough, you will have a losing trade When it comes to trading, you d better be able to say, Oh no, bad trade Let s find another one That is another reason why he never makes a trade that goes beyond his comfort zone Williams summarizes how he manages risk by using Einstein s famous equation, E MC2 Einstein had to be a commodity trader, he quips, because that s a commodity equation E emotion M money C the number of contracts you have squared So your emotions are equal to the amount of money you have and the contracts you spread them out on He explains that some people can withstand a 20 percent loss and be emotionally fine, while others can t You have to find your emotional quotient At what point can you go to sleep with that percentage of your account at risk Some people can t handle it So you d better find what works for you so you don t become irrational and do the wrong things because of your emotions.

(12)

when a bias estimate is available For this simple example, = 0 Sumb b marizing the results of the above analysis, the model of the time variation of the navigation error is p v = v = a a (13)

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