Satellite Orbit and Ephemeris Determination using Inter Satellite Links
Satellite Orbit and Ephemeris Determination using Inter Satellite Links Satellite Orbit and Ephemeris Determination using Inter Satellite Links
Autonomous Onboard ProcessingInter Satellite LinksFrom experiences made during this project regarding execution times of different softwaremodules, and under the assumption that the CPU is approximately 20 times slower than a 350MHz Pentium II, a very rough estimate can be derived for the required computational poweronboard a satellite:~ 100 ms for orbit determination (including non-linear state prediction) per measurementepoch (realtime)~ 30 ms for orbit propagation per epoch, i.e. 1 s to generate 50 trajectory points ahead,separated 144 s. (offline)~ 10 s for fitting a 2-hour-valid broadcast ephemeris over approximately 50 trajectory points.(offline)~ 2 ms for orbit propagation per epoch using broadcast ephemeris force model (for othersatellites in constellation); for 20 ISL’s requiring 40 ms. (realtime)~ 50 ms to perform a RAIM-like algorithm using 20 ISL’s (realtime)This results in approximately 200 ms for the tasks which have to be performed in realtime, i.e.once per second, in order to achieve integrity requirements. The remaining 800 ms per onesecond-duty-cyclecan be used to perform sequentially the (slightly more than) 10 s offlinetask. The 2-hour-valid broadcast ephemeris could updated, say every 30 minutes and wouldrequire less than 6 ms of computing time per one-second-duty-cycle, i.e. 0.6% of the availablecomputing power.Note that this is a very rough preliminary estimate, but it seems to be feasible to perform allthese tasks, required for full autonomous onboard processing with 20% – 25% of the availablecomputing power.7.2.2 Onboard Processing using ISLsInter satellite links are per definition measurements which are taken onboard and thereforeseem to perfectly match the requirements for an onboard processor. But ISL’s bear someproblems for a constellation consisting of autonomous processing satellites.The optimal approach to process ISL's would be, to process all measurements and all satellitesstates in one large filter. This is hard to achieve, if each satellite has its own state estimatoronboard. The following example shall highlight how satellite state estimates get correlated bythe inter satellite links.Let us assume 3 satellites , represented by their state X 1,2,3 . The measurements are processedtogether in on Kalman filter some other least squares estimator. The state transition of allthree satellites can be written as⎡x1⎤ ⎡Φ10 0 ⎤ ⎡x1⎤Eq. 7.2-1⎢x⎥ ⎢2020⎥ ⎢x⎥⎢ ⎥=⎢Φ⎥⋅⎢2⎥⎢⎣x ⎥ ⎢3 ⎦ ⎣ 0 0 Φ ⎥ ⎢3 ⎦ ⎣x⎥3 ⎦kkk−1Page 156R. Wolf
Inter Satellite LinksAutonomous Onboard ProcessingUp to that point, the covariances of the satellites are assumed to be uncorrelated.⎡pP =⎢⎢0⎢⎣011p02200 ⎤0⎥⎥p ⎥33 ⎦Eq. 7.2-2Now, satellite number one is transmitting an inter satellite ranging signal which is received bysatellites number two and tree. Therefore, the measurement equation system is written as⎡z⎢⎣z1312⎤⎥⎦k⎡h= ⎢⎣h1121h022h13⎤0⎥⎦k⎡x⋅⎢⎢x⎢⎣x123⎤⎥⎥⎥⎦k−1,⎡r13R = ⎢⎣ 00 ⎤r⎥12 ⎦Eq. 7.2-3with R being the covariance matrix of the (uncorrelated) measurements. The indices for themeasurements z and variances r represent the link direction, i.e. z 13 means "link from satelliteone to satellite three". Let us now only look at the equation concerning the Kalman gainmatrix,K = PHTT −( HPH + R) 1Eq. 7.2-4which is a 3 x 2 matrix. The element K jk contains the effect of the k th measurement on the j thstate. Performing the equation using our presumptions above leads to a lengthy expression.Here, we only concentrate on a few elements. K 32 contains the effect of the measurementbetween sat1 and sat2 (measured by sat2) on the state of sat3. The expression is none-zero andrequires all partial matrices to be evaluated.( h h h p p ) det(Inv)K32 −11 13 21 11 33⋅with= Eq. 7.2-51det(Inv) = Eq. 7.2-6h + + + + + + +2 22 22 2222211h22p11p22h13h21p11p33h13h22p22p33h11p11r12h13p33r12h21p11r13h22p22r13r12r13The problem is that the measurement sat1-sat2 is not available at sat3. The Kalman gain onthe state of sat2 evaluates to22( h p + h p + r ) det(Inv)2K22= h22p22 11 11 13 33 13⋅Eq. 7.2-7if all three satellites are processed in one filter.Let us assume now that we split the filter and process the measurement sat1-sat2 and sat1-sat3independently in two separate filters.R. Wolf Page 157
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<strong>Inter</strong> <strong>Satellite</strong> <strong>Links</strong>Autonomous Onboard ProcessingUp to that point, the covariances of the satellites are assumed to be uncorrelated.⎡pP =⎢⎢0⎢⎣011p02200 ⎤0⎥⎥p ⎥33 ⎦Eq. 7.2-2Now, satellite number one is transmitting an inter satellite ranging signal which is received bysatellites number two <strong>and</strong> tree. Therefore, the measurement equation system is written as⎡z⎢⎣z1312⎤⎥⎦k⎡h= ⎢⎣h1121h022h13⎤0⎥⎦k⎡x⋅⎢⎢x⎢⎣x123⎤⎥⎥⎥⎦k−1,⎡r13R = ⎢⎣ 00 ⎤r⎥12 ⎦Eq. 7.2-3with R being the covariance matrix of the (uncorrelated) measurements. The indices for themeasurements z <strong>and</strong> variances r represent the link direction, i.e. z 13 means "link from satelliteone to satellite three". Let us now only look at the equation concerning the Kalman gainmatrix,K = PHTT −( HPH + R) 1Eq. 7.2-4which is a 3 x 2 matrix. The element K jk contains the effect of the k th measurement on the j thstate. Performing the equation <strong>using</strong> our presumptions above leads to a lengthy expression.Here, we only concentrate on a few elements. K 32 contains the effect of the measurementbetween sat1 <strong>and</strong> sat2 (measured by sat2) on the state of sat3. The expression is none-zero <strong>and</strong>requires all partial matrices to be evaluated.( h h h p p ) det(Inv)K32 −11 13 21 11 33⋅with= Eq. 7.2-51det(Inv) = Eq. 7.2-6h + + + + + + +2 22 22 2222211h22p11p22h13h21p11p33h13h22p22p33h11p11r12h13p33r12h21p11r13h22p22r13r12r13The problem is that the measurement sat1-sat2 is not available at sat3. The Kalman gain onthe state of sat2 evaluates to22( h p + h p + r ) det(Inv)2K22= h22p22 11 11 13 33 13⋅Eq. 7.2-7if all three satellites are processed in one filter.Let us assume now that we split the filter <strong>and</strong> process the measurement sat1-sat2 <strong>and</strong> sat1-sat3independently in two separate filters.R. Wolf Page 157