CET 318 The Ninth Lecture 1. Data Pre-Processing 9. Data Processing(1) Book:p.203-276 Dr Guoding Zhou 1.1 Data Handling 1. 2 Cycle Slip Detection and Repair 2. Data Management 1. Definition of Cycle Slips Data Exchang During tracking, at a given epoch, the observed accumulated L. RINEX format consists of 2+)φ 2. RINEX Data Form The initial integer number n of cycles between the satellite ata file and the receiver is unknown. This phase remains constant as long as no loss of the signal lock occurs In this event, the integer counter is re-initialized which auses a jump in the instantaneous accumulated phase by integer number of cycles. This jump is called cvcle slip Header section Data section 2. Causes of Cycle Slips 3. Testing Quantities 1. Obstructions of the satellite signal due to trees. buildings The formulation of testing quantities is based on measured bridges, mountains, etc. This source is the most frequent carrier phases and code ranges. high receiver dynamics, or low satellite elevation Single frequency phase(Ll or L2)Raw phase celver Single frequency phase and code Phase and code range 4. Malfunctioning satellite ingle frequency phase and oscillators, but these cases are Single receiver tests enable in situ cycle slip detection and epair by the internal software of the receiver
1 Dr. Guoqing Zhou 9. Data Processing (1) CET 318 Book: p. 203-276 1. Data Pre-Processing 1.1 Data Handling 1. Downloading 2. Data Management 3. Data Exchange 1. RINEX format consists of: (1) The observation data file containing the range data; (2) The meteorological data file; and (3) The navigation message file. 2. RINEX Data Format 1. RINEX observation data file • Header section • Data section 2. RINEX meteorological data file • Header section • Data section 3. RINEX navigation message file • Header section • Data section 1.2 Cycle Slip Detection and Repair 1. Definition of Cycle Slips During tracking, at a given epoch, the observed accumulated phase ∆ϕ ∑ ∑ = = = = = + + i t i t N i 1 i 1 ∆ϕ 2π ϕ The initial integer number N of cycles between the satellite and the receiver is unknown. This phase ambiguity N remains constant as long as no loss of the signal lock occurs. In this event, the integer counter is re-initialized which causes a jump in the instantaneous accumulated phase by an integer number of cycles. This jump is called cycle slip. 2. Causes of Cycle Slips 1. Obstructions of the satellite signal due to trees, buildings, bridges, mountains, etc. This source is the most frequent one. 2. Low SNR due to bad ionospheric conditions, multipath, high receiver dynamics, or low satellite elevation. 3. Failure in the receiver software, which leads to incorrect signal processing. 4. Malfunctioning satellite oscillators, but these cases are rare. 3. Testing Quantities The formulation of testing quantities is based on measured carrier phases and code ranges. Phase and integrated Doppler combination Single frequency phase and Doppler Phase and code range combination Single frequency phase and code range Dual frequency phases (L1 and L2) Phase combinations Single frequency phase (L1 or L2) Raw phase Required data Testing quantity Single receiver tests enable in situ cycle slip detection and repair by the internal software of the receiver. For a single site, the testing quantities are
4. Detection and Repair 2. Determination of Size of Cycle Slip 1. Detection of Cycle Slip Interpolation Techniques: A method is to fit a curve through Each of the described testing quantities allows the location of testing quantities before and after the cycle s values. This also yields an approximate size of the cycle slip. Linear regression One of the methods for cycle slip detection is the scheme of Prediction Method: At a certain epoch, the function value (i.e one of the testing quantities)for the next epoch is predicted based on the information obtained from preceding function Kalman filtering 3. Repair of Cycle Slip 1. Based on the measurement noise assumption, the separation of the cycle slips is unambiguosuly possible for up to +4 cycles 2. Ambiguity Resolution 2. A smaller measurement noise increase t 3. For a larger cycle slips, another method should be used in order to avoid wrong choices in ambiguous 2.1 General Aspects 2. Double-Differences 1. Ambiguity Measurement The use of double-differences instead of single-differences The ambiguity inherent with phase measurements depends for carrier phase processing can eliminate the clock terms upon both receiver and satellite. The model for phase is nd the isolation of the ambiguities is possible 3. Effect of ionosphere, Troposphere and Other d=-p+fA6+N-△ Minor f we consider the ambiguity, N as an integer value, th For high accuracy of the carrier phase observable, the ambiguity is said to be resol ved or fixed. ambiguities must be resolved to their correct integer value since one cycle on the LI carrier may translate, in the Ambiguity fixing strengthens the baseline solution, but naximum, to a 19 cm position error. It should be stressed sometimes solutions with fixed ambiguities (i.e, integer here that integer ambiguity resolution may not always lues)and within a few millimeters Why
2 4. Detection and Repair Each of the described testing quantities allows the location of cycle slips by checking the difference of two consecutive epoch values. This also yields an approximate size of the cycle slip. One of the methods for cycle slip detection is the scheme of differences. 1. Detection of Cycle Slip Interpolation Techniques: A method is to fit a curve through the testing quantities before and after the cycle slip. Prediction Method: At a certain epoch, the function value (i.e., one of the testing quantities) for the next epoch is predicted based on the information obtained from preceding function values. 2. Determination of Size of Cycle Slip • Kalman filtering • Linear regression • Least square method 3. Repair of Cycle Slip Cycle slip repair using the ionospheric residual are 1. Based on the measurement noise assumption, the separation of the cycle slips is unambiguosuly possible for up to ±4 cycles. 2. A smaller measurement noise increase the separability. 3. For a larger cycle slips, another method should be used in order to avoid wrong choices in ambiguous situation. 2. Ambiguity Resolution 2.1 General Aspects Iono λ 1 ρ f δ N - λ 1 Φ = + ∆ + ∆ The ambiguity inherent with phase measurements depends upon both receiver and satellite. The model for phase is If we consider the ambiguity, N as an integer value, the ambiguity is said to be resolved or fixed. Ambiguity fixing strengthens the baseline solution, but sometimes solutions with fixed ambiguities (i.e., integer values) and float ambiguities (i.e., real values) may agree within a few millimeters 1. Ambiguity Measurement The use of double-differences instead of single-differences for carrier phase processing can eliminate the clock terms and the isolation of the ambiguities is possible. Why? 2. Double-Differences For high accuracy of the carrier phase observable, the ambiguities must be resolved to their correct integer value since one cycle on the L1 carrier may translate, in the maximum, to a 19 cm position error. It should be stressed here that integer ambiguity resolution may not always be possible. 3. Effect of Ionosphere, Troposphere and Other Minor
4. Satellite Geometry and Measurement Time 5. Multipath and Length of Baseline Satellite Geometry: The number of satellites tracked at ultipath is station dependent, it may be significant for any instant translates into a better dilution of precision nort baselines. As in the case of atmospheric and orbital alue. Thus if a receiver tracks seven or eight satellites. it Tors for long baselines, multipath has the effect of both is preferable since redundant satellites aid in the efficiency ontaminating the station coordinates and ambiguities and reliability of ambiguity resolution Multipath is also a critical factor for ambiquity resolution. Length of measurement Time: The information content of the carrier phase is a function of time which is directly 6. Major Steps of Ambiguity Resolution correlated to the movement of the satellite The Main First Step is the generation of potential integer The time is a critical component of ambiguity resolution mbiguity combinations that should be considered by the even under good geometric conditions. Example: every 15 seconds for one hour, every second for A combination is comprised of an integer ambiquity for. four minutes, a total of 240 measurements per satellite. egeach of the domble-difiference satellite nairs. Search Space is the volume of uncertainty which surrounds the approximate coordinates of the unknown The Second Major Step is the identification of the antenna location correct integer ambiguity combination. Static positioning: it can be realized from the so- called float ambiguity solution, The Third Major Step is the a validation(verification) Kinematic positioning: it is realized from a code of the ambiguities ange solution ize of the Search Space will affect the efficiency, i.e Integer ambiguity combinations to assess, which in tum ncreases the computational burden. This is typically important for kinematic applications. It is necessary to balance computational load with a conservative search space sIze 2.2 Basic Approaches Characteristics of Single-Frequency: 1. With Single Frequency Phase Data For only one frequency(Ll or L2)available, the most direct 1. The unmodeled errors affect all estimated parameters Therefore the integer nature of the ambiguities is lost and 1. Establishing model by Eq(9.14), hey are estimated as real values. teger values, a sequential adjustment could be performed ordinates, clock parameters, etc. )is estimated along with N in a common adjustment. 3. After an initial adjustment, the ambiguity with a computed value closest to an integer and with minimum standard When using double-differences over short baselines, this approach is sually successful. The critical factor is the ionospheric refraction bias is then fixed, and the adjustment is repeated(with one which must be modeled and which may prevent a correct resolution less unknown) to fix another ambiguity and so on
3 Satellite Geometry: The number of satellites tracked at any instant translates into a better dilution of precision value. Thus, if a receiver tracks seven or eight satellites, it is preferable since redundant satellites aid in the efficiency and reliability of ambiguity resolution. Length of Measurement Time: The information content of the carrier phase is a function of time which is directly correlated to the movement of the satellite. 4. Satellite Geometry and Measurement Time The time is a critical component of ambiguity resolution even under good geometric conditions. Example: every 15 seconds for one hour; every second for four minutes, a total of 240 measurements per satellite. Since multipath is station dependent, it may be significant for short baselines. As in the case of atmospheric and orbital errors for long baselines, multipath has the effect of both contaminating the station coordinates and ambiguities 6. Major Steps of Ambiguity Resolution Multipath is also a critical factor for ambiguity resolution. 5. Multipath and Length of Baseline The Main First Step is the generation of potential integer ambiguity combinations that should be considered by the algorithm. A combination is comprised of an integer ambiguity for, e.g., each of the double-difference satellite pairs. Search Space is the volume of uncertainty which surrounds the approximate coordinates of the unknown antenna location. • Static positioning: it can be realized from the socalled float ambiguity solution, • Kinematic positioning: it is realized from a code range solution. Size of the Search Space will affect the efficiency, i.e., computational speed. • A larger search space gives a higher number of potential integer ambiguity combinations to assess, which in turn increases the computational burden. This is typically important for kinematic applications. It is necessary to balance computational load with a conservative search space size. The Second Major Step is the identification of the correct integer ambiguity combination. The Third Major Step is the a validation (verification) of the ambiguities. 2.2 Basic Approaches 1. With Single Frequency Phase Data For only one frequency (L1 or L2) available, the most direct approach is. 1. Establishing model by Eq. (9.14), 2. Linearizing the model, 3. Solving the model, a number of unknowns (e.g., point coordinates, clock parameters, etc.) is estimated along with N in a common adjustment. When using double-differences over short baselines, this approach is usually successful. The critical factor is the ionospheric refraction which must be modeled and which may prevent a correct resolution of all ambiguities. 1. The unmodeled errors affect all estimated parameters. Therefore, the integer nature of the ambiguities is lost and they are estimated as real values. 2. To fix ambiguities to integer values, a sequential adjustment could be performed. 3. After an initial adjustment, the ambiguity with a computed value closest to an integer and with minimum standard error is considered to be determined most reliably. This bias is then fixed, and the adjustment is repeated (with one less unknown) to fix another ambiguity and so on. Characteristics of Single-Frequency:
2. With Dual Frequency Phase Data Why Dual Frequency: because of the various possible linear combinations Wide Lane and Narrow Lane Techniques: The adjustment based on the wide lane model gives wide laI ambiguities NLl-L2 which are more easily resoled than the base Wide Lane Signal: w=du-2 Significant ase compared To compute the ambiguities for the measured phases, we can get f12=34782MHz,M=862cm 入ag=19-244cm The increased wide lane wavelength provides an increased L-2 ambiguity spacing. This is the key to easier resolution of the integer ambiguities What is b? please see p. 105, Eg.6.73 Geometry-free linear Characteristics of Dual-Frequency 3. By Combining Dual Frequency Carrier Phase and Code Data The disadvantage of this combination is The most unreliable factor of the wide lane technique is The corresponding ambiguity is no longer an the influence of the ionosphere which increases with baseline length. This drawback can be eliminated by a ombination of phase and code data. The ionosphere is a problem or the ionospheric Φ1=a1--+N influence is eliminated which destroys the integer b nature of the ambiguities carrier phases The use of other linear combinations ranging from b? please narrow lane with a 10. 7 cm wavelength to extra wide th a 172. 4 cm wavelength. 4 equations contain 4 unknowns, geometry By a series derivation, we finally get 4. By Combining Triple Frequency Carrier Phase and code data Ju-f fu+r(Ru+ This technique for ambiguity resolution based on three arriers is denoted as Three- Carries Ambiguity Resolution-TCAR This rather of the wide ty NLI-L2 for each epoch and each Similarly Du=aa-2+Nu ite. It is independent of the baseline length and of the carrier phases ionospheric ef What is a Note that even if all systematic effects cancel out, the code ranges multipath effect remains and affects phase and code differently Eq6.74 R2=a/5+ 6 equations contain 5 fus unknowns
4 2. With Dual Frequency Phase Data Why Dual Frequency: because of the various possible linear combinations. fL1-L2 = 347.82 MHz, λL1-L2 = 86.2 cm λorg = 19~24.4 cm Wide Lane and Narrow Lane Techniques: Wide Lane Signal: Φ w = Φ L1 − Φ L 2 Significant increase compared to the original wavelengths • The increased wide lane wavelength provides an increased ambiguity spacing. • This is the key to easier resolution of the integer ambiguities. Φ L1− L 2 = Φ L1 − Φ L 2 L1 L 2 L1 L 2 f = f − f − NL1−L 2 = NL1 − NL 2 The adjustment based on the wide lane model gives wide lane ambiguities NL1-L2 which are more easily resoled than the base carrier ambiguities. ( ) (1 ) 2 1 1 2 1 1 2 1 1 1 1 2 1 2 L L L L L L L L L L L L L L f f f b f b f f N = Φ − Φ − N + − − − − − − To compute the ambiguities for the measured phases, we can get What is b? please see p. 105, Eq.6.73 1 2 1 2 1 2 1 1 1 1 2 1 2 ( ) L L L L L L L L L L L L L f f f f b f f N N + = Φ − Φ − + − − − After ionospheric Geometry-free linear phase combination The disadvantage of this combination is • The corresponding ambiguity is no longer an integer. • The ionosphere is a problem or the ionospheric influence is eliminated which destroys the integer nature of the ambiguities. The use of other linear combinations ranging from narrow lane with a 10.7 cm wavelength to extra wide with a 172.4 cm wavelength. Characteristics of Dual-Frequency: 3. By Combining Dual Frequency Carrier Phase and Code Data The most unreliable factor of the wide lane technique is the influence of the ionosphere which increases with baseline length. This drawback can be eliminated by a combination of phase and code data. 1 1 1 1 N b a L L L L f Φ = f − + 1 1 1 b a L L L f R = f + carrier phases code ranges 2 2 2 2 N b a L L L L f Φ = f − + 2 2 2 b a L L L f R = f + What is a, b? please see p. 105, Eq.6.74 4 equations contain 4 unknowns, geometry term, a and ionosphere term b and ( )( ) 1 2 1 2 1 2 1 2 1 2 L L L L L L L L L L R R f f f f N + + − − = Φ − − This rather elegant equation allows for the determination of the wide lane ambiguity NL1-L2 for each epoch and each site. It is independent of the baseline length and of the ionospheric effects. By a series derivation, we finally get Note that even if all systematic effects cancel out, the multipath effect remains and affects phase and code differently. 4. By Combining Triple Frequency Carrier Phase and Code Data This technique for ambiguity resolution based on three carriers is denoted as Three-Carries Ambiguity Resolution-TCAR. 1 1 1 1 N b a L L L L f Φ = f − + 1 1 1 b a L L L f R = f + carrier phases code ranges 2 2 2 2 N b a L L L L f Φ = f − + 2 2 2 b a L L L f R = f + What is a, b? please see p. 105, Eq.6.74 5 5 5 5 N b a L L L L f Φ = f − + 5 5 5 b a L L L f R = f + Similarly 6 equations contain 5 unknowns
1. A Standard Approach When processing the data based on double-differences by least quare adjustment, the ambiguities are estimated as real or floating point numbers. The first double-difference solution is called the float 3. Search Techniques mbiauitv solution The output is the best estimate of the tation coordinates as well as double- difference ambiguities Shore baseline(e.g, 5km), and long observation span(I hr), the float ambiguities close to integers. ity resolution in this case is merely used to refine the 2. Ambiguity Resolution On-the-Fly AROF OTF, OTRI The OTF techniques have common features like Code ranges are used to define the search space for the g, the determination of an initial solution. A nematic case. A relative code range position is used as the summary of the main features is given in Table 9.5 best estimate of antenna location, and the associated standard viations are used to define the size of the search space (a Please see the Table 9.5, p. 228. To reduce the number of integer ambiguity combinations, the code solution should be as accurate as possible which means Narrow correlator-type code ranges They have a resolution at 10 cm range and improved multipath reduction compared with standard C/A-code receivers 3. Ambiguity Function Method 4. Least Squares Ambiguity Search The basic principle by Counselman and Gourevitch is Technique =p()+N-Aa()4-ip()=N-5an L. The ry group consists of four satellites, which should have a good PDOP, the possible ambiguity sets are 231()=3a()_12N1-12sb(o 2. The remaining secondary satellites are used to eliminate candidates of the possible ambiguity sets cosα+is for the position e linearization of the observation equation) which may be obtaned real ax e range solution. Search Area: The search area may be established by surrounding the 2()-Pka(1)_-2aa() approximate position by a 38 region
5 3. Search Techniques 1. A Standard Approach When processing the data based on double-differences by least squares adjustment, the ambiguities are estimated as real or floating point numbers. The first double-difference solution is called the float ambiguity solution. The output is the best estimate of the station coordinates as well as double-difference ambiguities. Shore baseline (e.g., 5km), and long observation span (1 hr), the float ambiguities close to integers. Ambiguity resolution in this case is merely used to refine the achievable positioning accuracy. For example: 2. Ambiguity Resolution On-the-Fly (AROF, OTF, OTR) Code ranges are used to define the search space for the kinematic case. A relative code range position is used as the best estimate of antenna location, and the associated standard deviations are used to define the size of the search space (a cube, a cylinder, or an ellipsoid). To reduce the number of integer ambiguity combinations, the code solution should be as accurate as possible which means that receiver selection becomes important. • Low noise, • Narrow correlator-type code ranges They have a resolution at 10 cm range and improved multipath reduction compared with standard C/A-code receivers. The OTF techniques have common features like, e.g., the determination of an initial solution. A summary of the main features is given in Table 9.5 p. 228. Please see the Table 9.5, p. 228. 3. Ambiguity Function Method ρ ( ) N δ ( ) 1 AB j j AB t f t AB AB j Φ = + − λ ρ ( ) N δ ( ) 1 AB j j AB t f t AB AB j Φ − = − λ {2 πN ( ) 2ππf ( ) ρ ( )} λ 2π {2 πΦ ( ) AB j AB i t t i t t j AB j AB e e − − = 2 πN 2 πfδ ( )} ρ ( )} λ 2π {2 πΦ ( ) j AB j AB i t i i t t e e e j AB j AB − − = cos α sin α iα e = + i 2 πfδ ( ) ρ ( )} λ 2π {2 πΦ ( ) AB j AB i t i t t e e j AB − − = For one epoch and one satellite The basic principle by Counselman and Gourevitch is 4. Least Squares Ambiguity Search Technique Basic principles: is the separation of the satellites into a primary and a secondary group. 1. The primary group consists of four satellites, which should have a good PDOP, the possible ambiguity sets are determined. 2. The remaining secondary satellites are used to eliminate candidates of the possible ambiguity sets. Approximation: This technique requires an approximation for the position (due to the linearization of the observation equation) which may be obtained from a code range solution. Search Area: The search area may be established by surrounding the approximate position by a 3δ region
5. Fast Ambiguity Resolution Approach (FARA) In The First SteD: real values for double-difference The Main Characteristics of fara e estimated based on carrier phase (1)to use statistical information from the initial adjustme measurements and calculated by an adjustment procedure which also computes the (2)to use information of the var arance matrix to reject Cofactor matrix of the unknown parameters Posteriori variance of unit weight (a posteriori variance 3)to apply statistical hypothesis testing to select the correct set of integer ambiguities ce matrix of the un 1. computing the float carrier phase solution, Standard deviations of the ambiguities 3. computing a fixed solution for each ambiguity set, and 4. statistically testing the fixed solution with the smallest In The Second Step: the criteria for selecting possible ambiguity ranges(set) based on confidence intervals of the real In The Third Step: least squares adjustments with fixed values of the ambiguities. accepted ambiguity set yielding adjusted (1)First Criterion: The quality of the initial solution of the components and a posteriori variance factors irst step affects the possible ambiguity ranges. In more detail, the search range for this ambiguity is koN(k from In The Fourth Step I. The solution with the smallest a posteriori (2)A Second Criterion: the use of the correlation of the variance is further investigated 2. The baseline components of this solution are N=N-N compared with the float solution 3. If the solutio 4. The compatibility may be checked by a X teger sets. An even more impressive reduction is achieved if distribution which tests the compatibility of the a dual frequency phase measurements are available. sterioni variance with the a priori variance. 5. Fast Ambiguity Search Filter(FASF) 6. Least Squares Ambiguity Decorrelation Adjustment FASF is comprised of basically three components Teunissen proposed the idea and further developed the least ()A Kalman filter is applied to predict a state vector squares ambiguity decorrelation adjustment(LAMBDA) which is treated as observable Understanding the principle of this method must have a (2)The search of the ambiguities is performed at every strong background in linear algebra epoch until they are fixed, and are computed decorrelated related to ea Interested students can read through from p. 237-244
6 5. Fast Ambiguity Resolution Approach (FARA) The Main Characteristics of FARA (1) to use statistical information from the initial adjustment to select the search range, (2) to use information of the variance-covariance matrix to reject ambiguity sets that are not acceptable from the statistical point of view, and (3) to apply statistical hypothesis testing to select the correct set of integer ambiguities. The FARA Algorithm 1. computing the float carrier phase solution, 2. choosing ambiguity sets to be tested, 3. computing a fixed solution for each ambiguity set, and 4. statistically testing the fixed solution with the smallest variance. In The First Step: real values for double-difference ambiguities are estimated based on carrier phase measurements and calculated by an adjustment procedure which also computes the • Cofactor matrix of the unknown parameters • Posteriori variance of unit weight (a posteriori variance factor) • Variance-covariance matrix of the unknown parameters • Standard deviations of the ambiguities In The Second Step: the criteria for selecting possible ambiguity ranges (set) based on confidence intervals of the real values of the ambiguities. (1) First Criterion: The quality of the initial solution of the first step affects the possible ambiguity ranges. In more detail, the search range for this ambiguity is kδN (k from Student's t-distribution). (2) A Second Criterion: the use of the correlation of the ambiguities. 2 N N N 2 Nij Ni i j j Nij = Ni − N j δ = δ − 2δ + δ This criterion significantly reduces the number of possible integer sets. An even more impressive reduction is achieved if dual frequency phase measurements are available. In The Third Step: least squares adjustments with fixed ambiguities are performed for each statistically accepted ambiguity set yielding adjusted baseline components and a posteriori variance factors. In The Fourth Step: 1. The solution with the smallest a posteriori variance is further investigated. 2. The baseline components of this solution are compared with the float solution. 3. If the solution is compatible, it is accepted. 4. The compatibility may be checked by a X2- distribution which tests the compatibility of the a posteriori variance with the a priori variance. 5. Fast Ambiguity Search Filter (FASF) FASF is comprised of basically three components: (1)A Kalman filter is applied to predict a state vector which is treated as observable, (2)The search of the ambiguities is performed at every epoch until they are fixed, and (3)The search ranges for the ambiguities are computed recursively and are related to each other. 6. Least Squares Ambiguity Decorrelation Adjustment Teunissen proposed the idea and further developed the least squares ambiguity decorrelation adjustment (LAMBDA). Understanding the principle of this method must have a strong background in linear algebra. Interested students can read through from p. 237-244 21 12 11 12 Q Q Q Q 12 11 0 0 W decorrelated W
7. Ambiguity Determination with Special Constraints Background Knowledge Several multiple receiver methods for kinematic applications exist. re receivers at fixed locations(usually short distances apart) of the 4. Ambiguity validation the distance between tw can be used to increase the efficiency of the ambiguity In principle, the gain by using constraints results in a reduction of the potential ambiguity sets. 4. Ambiguity Validation After the determination of the integer ambiguities, it is Summary of interest to validate the quality of the quantities. There, the ainty of the What have we learnt? integer ambiguities is to be determined. Which parts are important? How do we validate the ambiguity? Ambiguity suceess rate For more information, please 47-248 Assignment 9 L What is the data format of rinex? Give the detailed description(15 point we test the quantities of the cycle slip? How do we reps he cycle slip?(20 point 3. List the basic approaches of ambiguity resolution(15 pace include what are their advantages and of research 6. What is the ambiguity determination of constraints(15
7 7. Ambiguity Determination with Special Constraints Several multiple receiver methods for kinematic applications exist. 1. One common procedure of this technique is to place two or more receivers at fixed locations (usually short distances apart) of the moving object. 2. Since the locations of the antennas are fixed, constraints (e.g., the distance between two antennas) may be formulated which can be used to increase the efficiency of the ambiguity resolution. In principle, the gain by using constraints results in a reduction of the potential ambiguity sets. Background Knowledge: 4. Ambiguity Validation After the determination of the integer ambiguities, it is of interest to validate the quality of the obtained quantities. There, the uncertainty of the estimated integer ambiguities is to be determined. How do we validate the ambiguity? • Ambiguity success rate For more information, please read p.247-248. 4. Ambiguity Validation Summary What have we learnt? Which parts are important? Assignment 9 1. What is the data format of RINEX? Give the detailed description (15 points) 2. What is cycle slip? What causes the cycle slip? How do we test the quantities of the cycle slip? How do we repair the cycle slip? (20 points) 3. List the basic approaches of ambiguity resolution (15 points). 4. Why do we use dual-frequency phase data to solve the ambiguities (15 points). 5. How many approaches does the determination of research space include? What are their advantages and disadvantages (20 points)? 6. What is the ambiguity determination of constraints (15 points)?
