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Before we begin building the circuit, take a few minutes to locate a clean well lit work table or work bench You will want to locate a small 27 to 33 watt pencil-tipped soldering iron, a roll of 60/40 tin/lead rosin core solder, and a small jar of Tip Tinner, a soldering iron tip cleaner/dresser Tip Tinner is available at a local Radio Shack store You will also need some small hand tools, such as a small flat-blade screwdriver, a Phillips screwdriver, a pair of small needle-nose pliers, a pair of tweezers, a magnifying glass and pair of end-cutters Once you have gathered all the tools, locate all the circuit diagrams, such as the schematics, the parts layout diagram, the resistor and capacitor code charts, etc Place all the component parts along with all your tools and diagrams in front of you, warm up your solder iron and we will start building the Radio Jupiter receiver project First we will identify the resistors for the project Refer to the chart shown in Table 22-1, which will help you to identify the resistor color codes Resistors usually have three or four color bands on the body of the resistor which denotes the resistor value The color bands begin at one end of the resistor body close to the edge The first color band on resistor R1 is blue, this denotes the digit six (6) The second color band is

Kalman gains or the steady-state Kalman gains Although this will lighten the on-line computational load, performance may su er For example, when steady-state gains are used, performance will su er during transient conditions such as start-up Second, the covariance equations allow prediction of the state estimation accuracy Therefore, it is often useful to perform covariance analysis in simulation prior to hardware construction to analyze implementation tradeo s or ensure satisfaction of system speci cations To summarize, assuming that P+ (0) is known, starting with k = 0, the steps of the covariance analysis are as follows for t [tk , tk+1 ) and k 0: 1 Specify the truth model: F, G, and Q 2 Specify the design model: F, G, and Q 3 Compute the discrete-time equivalent design model parameters: k and Qdk 4 Use eqn (66) to propagate the error covariance of the design model from tk to tk+1 5 At time tk+1 , increment k by one and compute H and H 6 Compute and store the Kalman lter gain sequence Kk using eqn (67) and compute the posterior design model error covariance using eqn (68) 7 Use the matrix parameters from the previous steps to process eqns (615 622) Iterate the above process over the time duration of interest or until steadystate is achieved Example 61 demonstrates the covariance analysis process Example 61 Consider the one dimension inertial frame INS aided by a biased position measurement The error equations for the INS are p 0 1 0 0 p 0 v = 0 0 1 0 v + v (623) a 0 0 0 0 a a b 0 0 0 b b p v 1 0 0 1 y = (624) a + b where var( ) = R = 10 m2 , P SD v = 25 10 3 m3 and P SD a = s 2 10 10 6 m5 The navigation mechanization equations are integrated in s.

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Answer 6-3

discrete-time using the small time step determined by the IMU to compute the navigation state The error state dynamics are modeled in continuoustime The position aiding measurement denoted by y is available at a 10Hz rate The measurement bias b is a scalar Gauss-Markov process with = 2 1 1 and P SD b = Qb = 4 m For a scalar Gauss-Markov process 300 sec s with these parameters, Pb Pb (t) Pb ( ) = 2 Pb + Qb = e 2 t Pb (0) + 1 e 2 t = Qb 2 Qb 2

We begin by factoring both the numerator and denominator into products of primes If the original numerator is negative, we attach an extra factor of 1, making sure all the prime factors are positive If the original denominator is negative, we do the same thing with it Next, we remove all the common prime factors from both the numerator and denominator Then we multiply all the factors in the numerator together, and do the same thing with the factors in the denominator If we end up with a negative denominator, we multiply both the numerator and the denominator by 1

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