Patent Grant
7966126
1. Field of the Invention
This invention pertains generally to systems for determining location and, more particularly, to systems for determining location and location uncertainty of railroad vehicles.
2. Background Information
In the art of railway signaling, traffic flow through signaled territory is typically directed by various signal aspects appearing on wayside indicators or cab signal units located on-board railway vehicles. The vehicle operators recognize each such aspect as indicating a particular operating condition allowed at that time. Typical practice is for the aspects to indicate prevailing speed conditions.
For operation of this signaling scheme, the track is typically divided into cascaded sections known as “blocks.” These blocks, which may be generally as long as about two to about five miles in length, are electrically isolated from adjacent blocks by typically utilizing interposing insulated joints. When a block is unoccupied, track circuit apparatus connected at each end are able to transmit signals back and forth through the rails within the block. Such signals may be coded to contain control data enhancing the signaling operation. Track circuits operating in this manner are referred to as “coded track circuits.” One such coded track circuit is illustrated in U.S. Pat. No. 4,619,425. When a block is occupied by a railway vehicle, shunt paths are created across the rails by the vehicle wheel and axle sets. While this interrupts the flow of information between respective ends of the block, the presence of the vehicle can be positively detected.
In the case of trains, control commands change the aspects of signal lights, which indicate how trains should move forward (e.g., continue at speed; reduce speed; stop), and the positions of switches (i.e., normal or reverse), which determine the specific tracks the trains will run on. In dark (unsignaled) territory, forward movement of trains is specified in terms of mileposts (e.g., a train is given the authority to move from its current location to a particular milepost along its planned route), landmarks or geographic locations. Sending the control commands to the field is done by an automated traffic control system, or simply control system. Control systems are employed by railroads to control the movements of trains on their individual properties or track infrastructures. Variously known as Computer-Aided Dispatching (CAD) systems, Operations Control Systems (OCS), Network Management Centers (NMC) and Central Traffic Control (CTC) systems, such systems automate the process of controlling the movements of trains traveling across a track infrastructure, whether it involves traditional fixed block control or moving block control assisted by a positive train control system.
In dark territory, controlling the movements of trains is effected through voice communication between a human operator monitoring the control system and the locomotive engineer. The interface between the control system and the field devices can either be through control lines that communicate with electronic controllers at the wayside that in turn connect directly to the field devices, or, in dark territory, through voice communication with a human, who manually performs the state-changing actions (e.g., usually switch throws).
It is known to employ a Global Positioning System (GPS) to determine the position of a train. For example, U.S. Pat. No. 4,899,285 discloses a system in which measurement results of a GPS position measuring apparatus are evaluated to determine whether they are reliable with respect to those derived by an integration calculation position measuring apparatus. The integration apparatus includes a direction sensor using a gyroscope or geomagnetic sensor and a vehicle speed sensor. Three GPS positions are sequentially measured, which correspond to three positions measured by the integration apparatus. The integration apparatus determines whether the measurement results of the GPS apparatus are twice continuously highly reliable. If so, then the integration apparatus adopts the subsequently measured GPS result as the reference position and executes the subsequent measurement of the position of the vehicle.
U.S. Pat. No. 5,129,605 discloses a wheel tachometer that generates pulses for a dead reckoning filter of a train control computer (TCC) to determine speed. The TCC compares velocity and position data, and rejects inconsistent data. A GPS receiver also generates a speed and position signal, which is input to the TCC to indicate position and speed, and also to calibrate the wheel tachometer. The TCC determines the best source of the speed signals. In making such determinations, the GPS speed is generally preferred when it is greater than ten miles per hour or when wheel slip is detected; otherwise, GPS calibrated wheel tachometer speed is used.
U.S. Patent Application Publication No. 2005/0065726 discloses that inertial sensors are subject to low frequency bias and random walk errors. Such errors grow in an unbounded manner upon integrating accelerometer and gyro output signals to obtain velocity and position, i.e., the computation has poor long-term stability. These long-term errors are corrected for by blending with D/GPS data, which possess comparatively excellent long-term stability. Conversely, a conventional navigator solution possesses good short-term stability, as the integration process tends to smooth high-frequency sensor errors (which are usually attenuated significantly by low-pass filtering), while D/GPS data has comparatively poor short-term stability due to, for example, multi-path effects and broadband noise. A train location system and method of determining track occupancy utilizes inertial measurement inputs, including orthogonal acceleration inputs and turn rate information, in combination with wheel-mounted tachometer information and GPS/DGPS position fixes to provide processed outputs indicative of track occupancy, position, direction of travel and velocity. Various navigation solutions are combined together to provide the desired information outputs using a Kalman filter or similar Bayesian estimator.
U.S. Pat. No. 5,902,351 discloses a vehicle tracking system including an inertial measurement unit having at least one gyro and at least one accelerometer, an odometer/tachometer, a GPS receiver, a tag receiver, and a map matching system. A Kalman filter may be utilized to reduce error within the vehicle tracking system and improve the accuracy thereof.
U.S. Pat. No. 5,893,043 discloses a process and an arrangement for determining the position of a vehicle moving on a given track by using a map matching process. At least three types of position measuring data in the form of object site data, path length data and route course data are obtained. A computer unit carries out, for each type of measuring data, a data correlation with a stored desired data quantity for the determination of position results, which are evaluated in an “m-out-of-n” decision making process. In this process, a given number “m” of the “n” determined position results is taken into account.
There is room for improvement in systems for determining location and location uncertainty of railroad vehicles.
This need and others are met by embodiments of the invention, which provide a vital system for determining location and location uncertainty of a railroad vehicle using a global positioning system receiver to determine position of the railroad vehicle, a predetermined track map of possible coordinates of the railroad vehicle, a plurality of motion sensors structured to determine change in location of the railroad vehicle, the motion sensors being biased to provide a positive bias error of the change in location of the railroad vehicle, and an acceleration sensor structured to determine acceleration of the railroad vehicle.
In accordance with an aspect of the invention, a system is for determining location and location uncertainty of a railroad vehicle. The system comprises: a global positioning system receiver structured to determine position of the railroad vehicle; a predetermined track map of possible coordinates of the railroad vehicle; a plurality of motion sensors structured to determine change in location of the railroad vehicle, the motion sensors being biased to provide a positive bias error of the change in location of the railroad vehicle; an acceleration sensor structured to determine acceleration of the railroad vehicle; and a processor cooperating with the global positioning system receiver, the predetermined track map, the motion sensors and the acceleration sensor to vitally determine the location and the location uncertainty of the railroad vehicle on the predetermined track map, the processor being structured to verify one of the motion sensors with another one of the motion sensors, determine a slip or slide condition of the railroad vehicle from the one of the motion sensors, determine speed and position of the railroad vehicle from the acceleration sensor during the slip or slide condition, verify the position of the railroad vehicle from the global positioning system receiver based upon the predetermined track map, and correct the positive bias error of the one of the motion sensors using the position of the railroad vehicle from the global positioning system receiver.
The processor may be structured to determine the location and the location uncertainty of the railroad vehicle in each of a plurality of periodic cycles.
The processor may be further structured to determine a tracking error from the difference between: (a) the position of the railroad vehicle from the global positioning system receiver for the current one of the periodic cycles, and (b) the location of the railroad vehicle for the previous one of the periodic cycles.
The processor may be further structured to determine the location uncertainty of the railroad vehicle in each of the periodic cycles; the processor may be further structured to determine the location uncertainty of the railroad vehicle for the current one of the periodic cycles from the sum of: (a) the location uncertainty of the railroad vehicle for the previous one of the periodic cycles, and (b) a predetermined constant times the change in location of the railroad vehicle from the one of the motion sensors; the track map may include a representation of a track for the railroad vehicle; the position of the railroad vehicle from the global positioning system receiver may have an uncertainty; the processor may be further structured to determine the tracking error only after the position of the railroad vehicle from the global positioning system receiver for a consecutive plurality of the periodic cycles satisfies both of: (a) a first condition defined by the position of the railroad vehicle from the global positioning system receiver as projected on the representation of a track being within: (i) a lower limit of the location of the railroad vehicle for the previous one of the periodic cycles minus the location uncertainty of the railroad vehicle for the current one of the periodic cycles, and (ii) an upper limit of the location of the railroad vehicle for the previous one of the periodic cycles plus three times the uncertainty of the global positioning system receiver along the representation of a track, and (b) a second condition defined by the position of the railroad vehicle from the global positioning system receiver as measured orthogonal to the representation of a track being within: (i) a lower limit of the location of the railroad vehicle for the previous one of the periodic cycles minus three times the uncertainty of the global positioning system receiver, and (ii) an upper limit of the location of the railroad vehicle for the previous one of the periodic cycles plus three times the uncertainty of the global positioning system receiver.
A full understanding of the invention can be gained from the following description of the preferred embodiments when read in conjunction with the accompanying drawings in which:
As employed herein, the term “number” shall mean one or an integer greater than one (i.e., a plurality).
As employed herein, the term “processor” means a programmable analog and/or digital device that can store, retrieve, and process data; a computer; a workstation; a personal computer; a microprocessor; a microcontroller; a microcomputer; a central processing unit; a mainframe computer; a mini-computer; a server; a networked processor; an on-board computer; or any suitable processing device or apparatus.
As employed herein, the term “vital” or “vitally” means that the acceptable probability of a hazardous event resulting from an abnormal outcome associated with a corresponding activity or thing is less than about 10−9/hour. Alternatively, the mean time between hazardous events is greater than 109 hours. Static data used by vital routines (algorithms), including, for example, track map data, have been validated by a suitably rigorous process under the supervision of suitably responsible parties.
As employed herein, the terms “railroad” or “railroad service” mean freight trains or freight rail service, passenger trains or passenger rail service, transit rail service, and commuter railroad traffic, commuter trains or commuter rail service.
As employed herein, the term “railroad vehicle” means freight trains, passenger trains, transit trains and commuter trains, or a number of cars of such trains or of a railroad consist.
As employed herein, the terms “carborne” and “carborne equipment” refer to things or equipment on-board a railroad vehicle.
The invention is described in association with a positive train control system, although the invention is applicable to a wide range of systems for determining the location and the location uncertainty of a railroad vehicle.
Referring to
The LDS 100 of
The block 113 of the LDS 100 is conventional and is used by conventional CAB signaling systems. The outputs 103,105 of the two respective tachometers 102,104 are input by an automatic train protection (ATP) system 114, as is also conventional. One of the tachometers 102,104 is a backup to and checks the other tachometer. Also, the accelerometer 106 is used to measure speed in conventional CAB signaling systems during slip/slide conditions. An acceleration function 116 and rate numerical integration function 118 calculate the corresponding speed (rate) 120 and distance (position) 122 of the railroad vehicle 11. The tachometer summation function 124 is an integration block that counts the pulses of the tachometer 102. The tachometer 102 is compared to the other tachometer 104 and is only used if they are within a suitable tolerance of each other. For example, the tachometer 102 outputs position change pulses 126 into the summation function 124. A ds/dt function 128 calculates speed 130 from the count of tachometer pulses divided by the sample time of the counting process. A dv/dt function 132 calculates the acceleration (speed changes) 134 over a relatively short time period. A selector function 136 checks the acceleration 134 against physical limits to determine if the tachometers 102,104 are slipping or sliding. If any slip or slide occurs, then the accelerometer 106 is used to calculate speed 120 and distance 122.
Known devices used for calculating distance are the tachometers 102,104 and the accelerometer 106. One tachometer 102 is the main device, while the other tachometer 104 is the secondary device. Two tachometer-indicated speeds 130 (only one is shown (e.g., V1); the second speed (e.g., V2) is used to validate the first speed (e.g., V1)) are compared (e.g., ΔV=V1−V2) to ensure that they are within a predetermined speed range (e.g., without limitation, ΔV<2 mph). Otherwise, if the change is higher than the predetermined value, then the train 11 is slipping, the tachometers 102,104 are not used to calculate speed 130 and distance 125, and the accelerometer 106 is used to determine the speed 120 and the distance 122. If slip/slide is detected by dv/dt function 134 and selector function 136, then the accelerometer 106 is used to calculate distance 122 during the slip/slide detection period.
The LDS 100 has a suite of sensors for estimating location, and takes advantage of the fact that the sensors are diverse and, thus, have different error characteristics. The tachometers 102,104 measure wheel rotation. The tachometer signal output 103,105 is pulses processed as a function of feet per pulse and wheel diameter (feet) to output distance traveled (feet). “Delta” distances accumulate to calculate the distance traveled. The wheel diameter entered into the LDS 100 is always rounded up and is periodically calibrated (e.g., without limitation, every 90 days). The entered wheel diameter used in the distance traveled calculations will always be greater than the actual wheel diameter. The wheel diameter “always greater” effect causes a predictable positive accumulated error in the distance traveled. Over time, as the wheel wears, the gain of the positive error increases. The error exhibits itself as percentage of distance traveled. The positive error is the dominant error over the relatively low random noise in the tachometers 102,104. The speed 130 is calculated from the distance traveled divided by the cycle time. The delta distance observation used is the highest delta distance of the two tachometers 102,104. Each cycle, the greater of the two distance traveled tachometer measurements is used as the input to the location update (Equation 6, below) variable LTach(N). Cross checking the two tachometers 102,104 before using their outputs provides an increased level of safety.
The inertial accelerometer 106 measures linear acceleration along the direction of travel plus a gravity component as a function of grade of the track 101 (
The GPS 110 calculates position from satellites orbiting the earth. The GPS position readings are used for initialization and corrections to the tachometer error in the LDS 100. As a non-limiting example, GPS position readings are received, for example, with about a one to two second delay. If the GPS receiver 110 gets a differential signal from a nearby base station, then the accuracy level increases. Differential lock and horizontal dilution of precision (HDOP) signals qualify the GPS data 144.
Differential lock is a flag from the GPS receiver 110, which flag sets the GPS uncertainty. One uncertainty is for non-differential GPS and a smaller uncertainty is for the GPS differential mode.
Dilution of precision (DOP) describes the geometric strength of a satellite configuration on GPS accuracy. When visible satellites are close together in the sky, the geometry is said to be weak and the DOP value is high; when far apart, the geometry is strong and the DOP value is low. Thus, a low HDOP value represents a better GPS horizontal positional accuracy due to the wider angular separation between the satellites used to calculate a GPS unit's position.
The uncertainty in the GPS readings is presumed to be seven feet for differential lock and 18 feet without. The HDOP affects the GPS uncertainty. A maximum HDOP is used to qualify the GPS data 144. Any readings above the HDOP are not used in the location calculations. The HDOP that corresponds to the final uncertainty chosen is used as criteria for rejecting GPS data 144. If a false differential lock is received, then the smaller uncertainty window will reject the GPS data 144 with a larger error.
The GPS 110 includes different internal modes, which output status data 140. A good data function 142 checks the GPS output status data 140 to determine if the GPS data 144 can be used. A Lon/Lat function 146, which may be the same as or similar to the CT subsystem 14 of
The 3σ R function 150 projects the GPS reading on the track map 108 to determine the GPS tracking error 107. The variable σ is the GPS position uncertainty or σGPS. The graphical function 154 shows graphically how the local track mapped coordinates 148 relate to the track map 108. If the output of the AND function 152 is true, then a GPS correction 155 is applied to the current position 156, as will be discussed. The collapse error function 158 and y % x dist function 160 show that the GPS correction 155 is applied to the current position 156, in order to correct tachometer distance error build up. The functions 160,162,164 can be determined by Equations 6 or 7 (for slip/slide conditions), below, as will be discussed. The Safe Braking Distance (SBD) calculation and SBD buffer 166 are part of the ATP system 114, which add any distances and/or position uncertainties to the location. The output 168 is the reported position of the railroad vehicle 11 and its uncertainty level. The LDS output 165 includes the distance and the speed of the railroad vehicle 11. The distance (position), as output by the LDS 100 at 165, is input and used by the SBD calculations 166 for the ATP system 114.
The track map 108 serves as a vital check to reject false GPS readings. The calculated location of the railroad vehicle 11 is always assumed to be on the track coordinates. The purpose of the GPS 110 is to “collapse” the accumulated distance error caused by the tachometers 102,104 and provide an initial position. The accumulated distance error is reduced with the lower limit being the uncertainty of the GPS position readings. The dominant predictable wheel diameter error characteristics provide a window for rejecting false GPS position readings in the direction of the track 101 (
The location accumulated error can only be corrected to the GPS uncertainty, since the GPS 110 serves as the initial location reference. As the distance traveled increases, eventually the accumulated error window 111B will be larger than the mean GPS tracking error 107 (i.e., estimated location perpendicular to the track minus the GPS position 109 projected on the track 101). When the GPS tracking error 107 is less than the accumulated error window for a number of consecutive readings, then the GPS tracking error 107 (Equations 1A, 1B and 2, below) corrects the location. A portion of the GPS tracking error 107 reduces the location uncertainty (Equations 9 and 10). The full GPS tracking error 107 is applied to the location estimate in Equation 6.
The LDS 100 includes a location update (Equations 6 or 7, below) and an uncertainty update (Equations 8A, 8B-8C, 9 or 10, below). The GPS corrections (location update) and uncertainty updates occur, for example, every second.
The location update of Equation 6 includes accumulating pulses from the highest output of the two tachometers 102,104 and applying the GPS tracking error 107 correction (Equations 1A or 1B, below). Crosschecks with both tachometers 102,104 verify the tachometer measurements. As a precondition to Equation 1A, the GPS tracking error 107 is checked to be within 3σGPS (three times the GPS uncertainty) of the track map 108 and within a location uncertainty window (Equations 8A or 8B-8C, below) along the direction of the track for six consecutive readings. If so, then the probability of the GPS position being not correct is about (1-0.989)6 (wherein the number 0.989 comes from the probability that a reading is within 3 sigma of its correct value) or about 1.77×10−12. The most significant error is the accumulated positive bias error in the tachometers 102,104. The random noise error of the tachometers 102,104 is small relative to the GPS position error; therefore, the GPS tracking error 107 (Equation 1A) has the same noise characteristics as the GPS position, but with the mean removed for short time periods.
The estimated location (Equations 6 or 7, below) is updated, for example, every second by incrementing the estimated location of the previous cycle (L(N-1)) with the tachometer distance (LTach(N)) (Equation 6). A cross check between the two tachometer readings validates that the two tachometer speed measurements agree to within, for example, ±2 mph for the speed 130 (
The location estimate uncertainty (LUW(N) or LUWP(N)) is the uncertainty of the previous cycle (LUW(N-1) or LUWP(N-1)) plus the accumulated tachometer error due to distance traveled (K2*LTach(N)) minus the GPS tracking error correction (0.2*|LGPSTrackErr(N)|). See Equations 8A and 9, below.
The uncertainty of the estimated location 165 (
The 1.5% accuracy of the tachometers 102,104 for short distances and the track map 108 with 3σGPS window establish the confidence level of the GPS position. As shown in
The GPS uncertainty (σGPS) is kept by requiring, for example, the six previous GPS readings to be inside the track map window 111A (3σGPS) and the location uncertainty window 111B (Equations 8A and 9).
The following variables are used in Equations 1-12, below:
L(N) is location estimate in map coordinates resolved to 7-foot fragments as part of blocklets; this location estimate is updated every cycle by the tachometer position change and GPS corrections, if available.
LTach(N) is tachometer position “delta” or the change in location measured each cycle from the highest output of the two tachometers 102,104.
LGPS(N) is GPS location projected onto the track 101.
L_hd GPSTrackErr(N) is GPS tracking error 107.
αDecel(N) is the measurement of the accelerometer 106.
K2 is location bias error coefficient (e.g., without limitation, 0.015) of the tachometers 102,104.
K3 is location bias error coefficient (e.g., without limitation, 0.05) of the accelerometer 106.
VSlip/Slide is slip/slide velocity change limit.
LUW(N) is location uncertainty window, which is initialized to 3σGPS
LUWP(N) is location uncertainty window positive side (the window grows asymmetrically for tachometer errors; during slip/slide, the uncertainty grows in both directions), which is initialized to 3σGPS. During non-slip/slide conditions, the uncertainty increases in the positive direction only due to the tachometer wheel diameter bias. During slip/slide conditions, the uncertainty increases equally in both directions.
σGPS is GPS uncertainty (e.g., without limitation, 7 feet; 18 feet for non-differential).
Ct is sample time (e.g., without limitation, 1 second).
N-1 is the previous cycle number.
N is the current cycle number.
V(N-1) is velocity of the previous cycle.
V(N) is velocity of the current cycle.
Equation 1A is evaluated if the following three conditions are true: (1) the last six GPS readings are in the window: L(N-1)−LUW(N)<GPS reading projected on the track map 108<L(N-1)+3σGPS along the track 101; (2) L(N-1)−3σGPS<GPS reading projected on the track map 108<L(N-1)+3σGPS orthogonal to the track 101; and (3) the qualifier window is affected in the positive direction during slip/slide conditions:
L(N-1)−LUW(N)<GPS reading<L(N-1)+LUWP(N), then: LGPSTrackErr(N)=LGPS(N)−L(N-1) (Eq. 1A)
else, Equation 1B is evaluated:
LGPSTrackErr(N)=0 (Eq. 1B)
In normal steady state conditions, the GPS tracking error 107 can be positive or negative, although it may be more negative than positive for certain periods of time.
The GPS tracking error limit is shown by Equation 2:
−|LGPSTrackErrLim(N)|≦LGPSTrackErr(N)≦|LGPSTrackErrLim(N)| (Eq. 2)
wherein:
LGPSTrackErrLim(N)=LTach(N) and LGPSTrackErrLim(N) is always greater than 20 (feet per cycle).
Hence, for computing the limits, a lower limit on the check is set to 20 feet per cycle.
Equation 3 provides a slip/slide condition check.
V(N)−V(N-1)>VSlip/Slide (Eq. 3)
If slip/slide exists, then Equation 4 sets the velocity V(N).
V(N)=V(N-1)+αDecel(N)*Ct (Eq. 4)
Otherwise, Equation 5 sets the velocity for non-slide conditions.
V(N)=LTach(N)/Ct (Eq. 5)
Equations 6 and 7 update the location for non-slide and slide conditions, respectively. The tachometer data is combined with the GPS data in Equation 6. This position update corrects the position for accumulated tachometer error. This equation essentially is the collapse error function 158 of
L(N)=L(N-1)+LTach(N)+LGPSTrackErr(N) (Eq. 6)
L(N)=L(N-1)+V(N-1)*Ct+αDecel(N)*Ct2/2+LGPSTrackErr(N) (Eq. 7)
In Equations 8A-8C, for the location uncertainty window update, only one of K2 or K3 is used at one time; K2 is set to zero for slip/slide conditions and, otherwise, K3 is set to zero. If the GPS reading is out of the window defined by the three conditions for Equation 1A, then either Equation 8A is used for non-slide conditions or Equations 8B-8C are used for slide conditions. The bounded error characteristics of the tachometers 102,104 are used to qualify the GPS data. In particular, the integrated tachometer pulses are used to calculate the window to reject GPS readings along the direction of the track 101 in Equation 8A.
LUW(N)=LUW(N-1)+K2*LTach(N) (Eq. 8A)
LUW(N)=LUW(N-1)+K3*|L(N)−L(N-1)| (Eq. 8B)
LUWP(N)=LUWP(N-1)+K3*|L(N)−L(N-1)| (Eq. 8C)
If the GPS reading is in the window defined by the three conditions for Equation 1A for at least the last six readings, then Equation 9 applies for non-slide conditions and Equation 10 applies for slide conditions.
LUW(N)=LUW(N)−0.2*|LGPSTrackErr(N)| (Eq. 9)
LUWP(N)=LUWP(N)−0.2*|LGPSTrackErr(N)| (Eq. 10)
Equations 11 and 12 provide the uncertainty low limit for slide conditions. Lower limits on the uncertainty windows are evaluated every cycle. If the value calculated is lower, then the value is set to the lower limit.
If LUW(N)≦3*σGPS, then LUW(N) is set to 3*σGPS (Eq. 11)
If LUWP(N)≦3*σGPS, then LUWP(N) is set to 3*σGPS (Eq. 12)
As can be seen by the low limit check of Equations 11 and 12, the GPS tracking error terms only correct the location and the uncertainty when the uncertainty is greater than the current GPS uncertainty (differential or non-differential). The uncertainty widow values are set to their lower limits in Equations 11 and 12.
If the slip/slide conditions are continuous for more than 30 seconds, then the LDS 100 is profiled to a stop for location reset to the GPS location projected on the track 101.
For zero speeds, the location uncertainty (qualifying) window returns to the GPS uncertainty and the location estimate returns to the GPS position. The effect of the lower limit on the uncertainty window, and the accuracy of the GPS and the location update of Equations 6 and 7 cause these results.
During movement, three times the GPS uncertainty is the lower limit of the location estimate uncertainty. When the railroad vehicle 111 is moving, the location estimate uncertainty window will always be greater than or equal to three times the GPS uncertainty.
The location estimate is initialized to the first GPS location that is within 3σGPS of the track 101. The location is initialized to the first GPS position that is near the track map 108. The reading is skipped if it is further than 3 sigma away from the track map 108.
On the other hand, if the location L(N) was previously initialized, as determined at 202, then it is determined if there is a slip/slide condition, at 210, as per Equation 3. If not, then, at 212, the location estimate L(N) and the location uncertainty window LUW(N) are updated per Equations 6 (ignoring, for the moment, the GPS tracking error 107 of Equation 1A) and 8A, respectively. Otherwise, if there is a slip/slide condition, then the location estimate L(N) and the location uncertainty windows LUW(N) and LUWP(N) are updated per Equations 7 (ignoring, for the moment, the GPS tracking error 107 of Equation 1A) and 8B-8C, respectively.
After either 212 or 214, it is determined, at 216, if the GPS data 144 is within the windows 111A,111B of
Next, at 222, the location estimate L(N) is updated with the (limited) GPS tracking error 107 of Equations 1A and 2 per Equation 6. Also, the location uncertainty windows LUW(N) and LUWP(N) are updated with the (limited) GPS tracking error 107 of Equations 1A and 2 per Equations 9 and 10, respectively.
Finally, at 224, the location uncertainty windows LUW(N) and LUWP(N) are adjusted, if needed, to be at least the lower limit of 3σGPS, after which the routine exits to await the next cycle, at 208.
While specific embodiments of the invention have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the invention which is to be given the full breadth of the claims appended and any and all equivalents thereof.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4731613 | Endo et al. | Mar 1988 | A |
| 4790191 | Shultz, Jr. | Dec 1988 | A |
| 4899285 | Nakayama et al. | Feb 1990 | A |
| 5129605 | Burns et al. | Jul 1992 | A |
| 5574469 | Hsu | Nov 1996 | A |
| 5862511 | Croyle et al. | Jan 1999 | A |
| 5867122 | Zahm et al. | Feb 1999 | A |
| 5893043 | Moehlenbrink et al. | Apr 1999 | A |
| 5902351 | Streit et al. | May 1999 | A |
| 5948043 | Mathis | Sep 1999 | A |
| 6127970 | Lin | Oct 2000 | A |
| 6128558 | Kernwein | Oct 2000 | A |
| 6218961 | Gross et al. | Apr 2001 | B1 |
| 6317688 | Bruckner et al. | Nov 2001 | B1 |
| 6345233 | Erick | Feb 2002 | B1 |
| 6373403 | Korver et al. | Apr 2002 | B1 |
| 6397130 | Carr et al. | May 2002 | B1 |
| 6434452 | Gray | Aug 2002 | B1 |
| 6449559 | Lin | Sep 2002 | B2 |
| 6456937 | Doner et al. | Sep 2002 | B1 |
| 6480766 | Hawthorne et al. | Nov 2002 | B2 |
| 6496778 | Lin | Dec 2002 | B1 |
| 6641090 | Meyer | Nov 2003 | B2 |
| 6681160 | Bidaud | Jan 2004 | B2 |
| 6826478 | Riewe et al. | Nov 2004 | B2 |
| 6879875 | Hu et al. | Apr 2005 | B1 |
| 6970774 | Kane et al. | Nov 2005 | B2 |
| 7200471 | Kane et al. | Apr 2007 | B2 |
| 20020045975 | Carr et al. | Apr 2002 | A1 |
| 20020062193 | Lin | May 2002 | A1 |
| 20030216865 | Riewe et al. | Nov 2003 | A1 |
| 20030236598 | Villarreal Antelo et al. | Dec 2003 | A1 |
| 20040267450 | Kernwein | Dec 2004 | A1 |
| 20050065726 | Meyer et al. | Mar 2005 | A1 |
| 20050107954 | Nahla | May 2005 | A1 |
| 20060030978 | Rajaram | Feb 2006 | A1 |
| 20070010940 | Tan et al. | Jan 2007 | A1 |
| 20090138199 | Bonanni et al. | May 2009 | A1 |
| Number | Date | Country | |
|---|---|---|---|
| 20090210154 A1 | Aug 2009 | US |
