Real time multi sensor integrity monitoring through sensor fusion for navigation purposes

Abstract

A method of providing a position, navigation, or timing solution, including acquiring one or more measurements from a plurality of sensors, determining an estimated error variance, a weight, and a severity impact of each sensor, calculating an individual sensor performance for each sensor, generating a fused sensor performance by adding the individual sensor performance of all sensors with a filter, generating a plurality of subcombination sensor performances with the filter, each sensor in a given sub-combination characterized by its individual sensor performance, and each sub-combination determined by adding weighted individual sensor performances of the individual sensors, comparing the plurality of sub-combination sensor performances with the fused sensor performance, each sub-combination sensor performance and the fused sensor performance having a confidence score based on the estimated error variance, the weight, and the severity impact of each sensor, and selecting a sensor performance with a predetermined confidence score.

Description

[0001] REAL TIME MULTI SENSOR INTEGRITY MONITORING THROUGH SENSOR

[0002] FUSION FOR NAVIGATION PURPOSES

[0003] BACKGROUND

[0004] In the field of navigation, different types of sensors can be utilized in sensor fusion to provide an assured and more accurate estimate of a PNT (Position, Navigation, and Timing) solution. Sensors typically used to provide PNT information include GPS / GNSS receivers, IMUs (inertial Measurement Units), Signals of Opportunity (SOP) receivers, precision clocks / oscillators, odometry, vision navigation, barometers, altimeters, etc. Sensor fusion algorithms typically rely on filtering, such as Kalman Filtering or Particle Filtering, to combine noisy sensor measurements into an optimal “fused” solution. The filters create dynamic weights for the input measurements based upon their values and associated uncertainty, but typically do not account for sensor integrity. A sensor that has been compromised through degradation, denial, or spoofing can adversely affect the fused solution and potentially cause inaccurate results.

[0005] Accordingly, methods and systems for providing accurate PNT solutions are needed.

[0006] SUMMARY

[0007] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0008] Disclosed herein is a method and system for determining a positioning, navigation, or timing (PNT) solution. In some embodiments, the system utilizes a single Kalman Filter configured to calculate a number of combinations of sensor information to calculate multiple PNT solutions concurrently. The multiple solutions include solutions from independent sensors and different subsets of the sensors. The calculated solutions are compared to the main fused solution for integrity scoring assessment. If a solution from an independent or subset of sensors is determined to be off from the main fused solution by a threshold, the solution from the subset of sensors without the compromised sensors is chosen as the new main solution to be reported by the system. DESCRIPTION OF THE DRAWINGS

[0009] The foregoing aspects and many of the attendant advantages of this invention will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein:

[0010] FIGURE 1 is an example system, in accordance with the present technology;

[0011] FIGURE 2 is another example system, in accordance with the present technology;

[0012] FIGURE 3 is an example method of generating a position, navigation, or timing solution, in accordance with the present technology; and

[0013] FIGURE 4 is a block diagram of an example system, in accordance with the present technology.

[0014] DETAILED DESCRIPTION

[0015] While illustrative embodiments have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the invention.

[0016] In some embodiments, the system calculates multiple PNT solutions without the use of multiple filters or algorithms competing for computation resources. The linear algebra within the Kalman Filter allows for additional rows and columns of data to be added with the different combinations of sensor information. This increases the complexity of the filter slightly, but it is still significantly less complex than using multiple independent filters. In different embodiments, different filters may be used, for example Particle Filter, Least Square Filter, Wiener Filter, etc.

[0017] As described herein, an individual sensor is defined as a single component that provides position, navigation and / or timing information. Additionally, sensors which improve the confidence of measurements or aid in direction finding such as magnetic compasses, barometer altitude estimators, or vision aiding sensors which provide heading guidance can be considered a sensor. In some embodiments, each individual sensor is continuously monitored against a predetermined integrity metric (also called a severity impact) specific to that sensor. Individual sensors that contribute enough information to formulate an independent PNT solution can also be treated as individual sensors.

[0018] As disclosed herein, a subset of sensors (or a sub-combination of sensor performances) is defined as a grouping of components that provide position, navigation and / or timing information. The subset of sensors is described by similar features (visual, radio frequency (RF), physical, etc.), available data provided (position, navigation, and / or timing), interference opportunities (magnetic, light interference, RF interference, multipath, none), and statistical performance analysis.

[0019] Both the individual sensors as well as a subset of the entire sensor suite provide different sensor performances. Each sensor performance is scored using at least an estimated error variance, a weight, and a severity impact to determine the overall “best” (optimal) solution. Each sensor’s contribution to a particular solution is given a confidence score. In some embodiments, this confidence score is then realized in the end subcombination sensor performance as an additive metric where each contribution is weighted appropriately and then summed together. In some embodiments, the sensor performance with the highest confidence score is selected as the PNT solution.

[0020] Each sensor performance is assigned a confidence score based on the severity impact and estimated uncertainty. The fused sensor performances are compared against each other, and individual sensors so that each can be evaluated for faulty output or degradation in performance due to interferencejamming or spoofing in the case of sensors like GPS / GNSS receivers.

[0021] If a sensor performance that is used is determined to be degraded or diverging from the other solution sets, it can be discounted, and another sensor performance can be used. The algorithm then selects among the best performing or least diverging or degraded fused sensor performance created from a subset of sensors. The algorithm can also be used to identify faulty sensors, diverging sensors or solutions, and sensors which are being externally degraded by interfering signals or conditions.

[0022] FIG. 1 is an example system 100, in accordance with the present technology. In some embodiments, the system 100 includes a platform 101. The platform 101 may include internal sensors 102A, 102B, at least one external sensor 103, and a controller 104. While two internal sensors 102A, 102B are illustrated, it should be understood that the system 100 may include any number of internal sensors 102. Further, it should be understood that while a single external sensor 103 is illustrated, the system 100 may include any number of external sensors 103. In addition, the internal 102 and external 103 sensors may be referred to collectively as sensors or a plurality of sensors herein. Accordingly, the term “sensors” or “plurality of sensors” can include internal sensors 102, external sensors 103, or a combination thereof. The platform 101 may take any form. In some embodiments, platform 101 is a vehicle. In such embodiments, such as when the platform 101 is a vehicle, the platform

[0023] 101 is mobile. In some embodiments, platform 101 is an airborne vehicle. In some embodiments, the platform 101 is a land vehicle. In some embodiments, the platform 101 is an aerospace vehicle. In some embodiments, the platform 101 is a car, an airplane, a helicopter, a satellite, a bus, a train, a truck, or the like.

[0024] In some embodiments, the system 100 includes a plurality of sensors (including internal sensors 102A, 102B, and the external sensor 103). In some embodiments, internal sensors 102 include accelerometers, gyroscopes, oscillators, wheel tick sensors, and the like. In some embodiments, the internal sensors 102A, 102B are different sensors, such as an accelerometer and a wheel tick sensor. In some embodiments, the internal sensors 102A, 102B include duplicate sensors, such as two accelerators. Any number of internal sensors

[0025] 102 may be included in system 100.

[0026] In some embodiments, external sensor 103 is an infrared, ultrasonic, optical, or proximity sensor. In some embodiments, external sensor 103 is a magnetometer (Hall Effect Sensor), barometer, temperature sensor, RF sensor, or the like. In some embodiments, such as when there are multiple external sensors 103, the external sensors

[0027] 103 are different sensors, such as an ultrasonic sensor and a proximity sensor. In some embodiments, the external sensors 103 include duplicate sensors, such as two ultrasonic sensors. Any number of external sensors 103 may be included in system 100.

[0028] In some embodiments, internal sensors 102A, 102B have a higher weight than the external sensor 103. In some embodiments, it is more difficult to impact the performance of the internal sensors 102A. 102B as described herein, because there is no physical access to the internal sensors 102A, 102B. In contrast, external sensors 103 perform by using external excitation, and thus could be physically accessed.

[0029] In some embodiments, the system 100 includes a controller 104. The controller includes a single Kalman Filter (as shown and described in detail in FIG. 2). In some embodiments, the controller 104 is electrically coupled to the plurality of sensors 102A, 102B, 103 with a wired or wireless connection. In operation, the controller 104 may acquire one or more measurements from the plurality of sensors 102A, 102B, 103 attached to the platform 101, determine an estimated error variance for each sensor 102A, 102B, and 103 of the plurality of sensors 102A, 102B, 103, determine a weight for each sensor 102A, 102B, and 103 of the plurality of sensors 102A, 102B, 103, and determine a severity impact of each sensor 102A, 102B, and 103 of the plurality of sensors 102A, 102B, 103. In some embodiments, the controller 104 is further configured to calculate an individual sensor performance for each sensor 102A, 102B, and 103 of the plurality of sensors 102A, 102B, 103 based on at least the estimated error variance, the weight, and the severity impact of each sensor 102A, 102B, and 103 in the plurality of sensors 102 A, 102B, 103. As described herein, the controller 104 includes a single Kalman Filter. In some embodiments, the controller is further configured to generate a fused sensor performance by adding the individual sensor performance of all sensors 102A, 102B, and 103 in the plurality of sensors 102A, 102B, 103 with the single Kalman Filter. In some embodiments, the controller 104 is further configured to generate a plurality of sub-combination sensor performances with the single Kalman Filter. In some embodiments, each sensor in a given sub-combination of sensor performances is characterized by its individual sensor performance, and each subcombination of sensor performances is determined by adding weighted individual sensor performances of the individual sensors. The controller 104 may then compare the plurality of sub-combination sensor performances with the fused sensor performance. In some embodiments, each sub-combination sensor performance and the fused sensor performance include a confidence score based on the estimated error variance, the weight, and the severity impact of each sensor in each sub-combination of sensors and all sensors in the plurality of sensors. The controller 104 may then select a sensor performance with a predetermined confidence score from the plurality of sub-combination sensor performances or the fused sensor performance as the PNT solution. In some embodiments, the predetermined confidence score is the highest confidence score of each subcombination sensor performance and the fused sensor performance.

[0030] FIG. 2 is an example system 200, in accordance with the present technology. In some embodiments, the system 200 includes a platform 201, a plurality of sensors 202 A, 202B, 202C, and a Kalman Filter 210. It should be understood that the Kalman Filter 210 may be part of a controller (such as controller 104 in FIG. 1).

[0031] In some embodiments, the plurality of sensors 202A, 202B, 202C include internal sensors, external sensors, or a combination thereof, as described herein. In some embodiments, the plurality of sensors 202A, 202B, 202C includes an accelerometer, a gyroscope, an oscillator, a wheel tick sensor, an infrared sensor, an ultrasonic sensor, an optical sensor, a camera, a LiDAR sensor, a proximity sensor, a magnetometer, a barometer, a temperature sensor, a radio frequency sensor, or a humidity sensor. While three sensors 202A. 202B, and 202C are illustrated, this is merely exemplary, and any number of sensors may be included in the plurality of sensors 202A, 202B, 202C.

[0032] In operation, the Kalman Filter 210 receives an individual sensor performance of each sensor of the plurality of sensors 202A, 202B, 202C. In some embodiments, a controller (such as controller 104) calculates the individual sensor performance for each sensor in the plurality of sensors 202A, 202B, 202C. The individual sensor performance may comprise at least an estimated error variance, a weight, and a severity impact of each sensor in the plurality of sensors 202A, 202B, 202C. In some embodiments, the Kalman Filter 210 generates a fused sensor performance 215. The fused sensor performance 215 includes the individual sensor performance for every sensor in the plurality of sensors 202A, 202B, 202C. In some embodiments, the same Kalman Filter 210 also generates subcombination sensor performances 220 A, 220B, 220C. In some embodiments, the Kalman Filter 210 generates all possible sub-combination sensor performances of m sensors from the plurality of sensors, n. For example, for the system 200 including three sensors, A, B, and C, as illustrated in FIG. 2, three sub-combinations are generated, a sub-combination of sensor A and sensor B 220A, a sub-combination of sensor B and sensor C 220B, and a subcombination of sensor A and sensor C 220C. It should be understood that the Kalman Filter 210 may generate all possible sub-combinations of sensor performances including any number of sensors. For example, in a system having five sensors, A, B, C, D, and E, the Kalman Filter 210 may generate ten unique sub-combinations including two of the five sensors (AB, AC, AD, AE, BC, BD, BE, CD, CE, DE), ten unique sub-combinations including three of the five sensors (ABC, ABD, ABE, ACD, ACE, ADE, BCD, BCE, BDE, CDE), and five unique sub-combinations including four of the five sensors (ABCD, ABCE, ABDE, ACDE, BCDE). Accordingly, the Kalman Filter 210 may utilize linear algebra to add additional rows, columns, or rows and columns for the sub-combinations of sensor performances. In this manner, a single Kalman Filter 210 can generate all possible subcombinations of sensor performances, without the use of multiple filters or algorithms. In some embodiments, the Kalman Filter 210 may calculate only some of the possible subcombinations, such as only sub-combinations having three sensors of the plurality of sensors. In some embodiments, the Kalman Filter 210 may be another type of filter, such as a Particle filter, a Least Square filter, or a Weiner filter.

[0033] Once the fused sensor performance 215 and each sub-combination of sensor performances 220A, 220B, and 220C are generated, the system 200 determines which sensor performance of the fused sensor performance 215 and the sub-combination of sensor performances 220A, 220B, 220C has a predetermined confidence score, or is closest to a predetermined confidence score. In some embodiments, the predetermined confidence score is an expected confidence score of the system 200 where every sensor in the plurality of sensors 202A, 202B, 202C is fully operational. In some embodiments, the predetermined confidence score is a highest confidence score from the plurality of subcombination sensor performances 220A, 220B, 220C or the fused sensor performance 215.

[0034] In some embodiments, the fused sensor performance 215 and the plurality of subcombination performances 220A, 220B, 220C are generated continuously. Accordingly, in such embodiments, the Kalman Filter 210 may switch the sensor performance selected in real time, based on which sensor performance from the fused sensor performance 215 or a sub-combination of the plurality of sub-combination performances 220A, 220B, 220C is the closest to a predetermined confidence score at any given time.

[0035] FIG. 3 is an example method 300 of generating a position, navigation, or timing solution, in accordance with the present technology.

[0036] In block 305, a system is configured with a plurality of sensors. The system may be any system described herein, such as system 100, system 200, or system 400. Accordingly, the plurality of sensors can be any plurality of sensors described or shown herein, such as plurality of sensors 102A, 102B, 103, plurality of sensors 202A, 202B, 202C, or plurality of sensors 402A, 402B, 402C. In some embodiments, the plurality of sensors includes one or more internal sensors, one or more external sensors, or a combination thereof.

[0037] In step 310, which includes blocks 310A, 310B, and 310C, the error variance, weight, and severity impact for each sensor in the plurality of sensors are determined. In some embodiments, the error variance, weight, and severity impact (also called “metrics” herein) are determined by a controller (such as controller 104). In some embodiments, blocks 310A, 310B, and 310C occur simultaneously. In some embodiments, blocks 310A, 310B, and 310C occur concurrently. In some embodiments, any one of blocks 310A, 310B, or 310C, or a combination thereof may be omitted from step 310.

[0038] In some embodiments, the error variance determined in block 310A is determined within one sigma. In some embodiments, the error variance is estimated based on research or data collected for each individual sensor, based on the type of sensor. For example, an odometer may have an error variance based on prior research or collected data from the odometer or other, similar odometers.

[0039] In block 31 OB, the weight of each sensor is determined. In some embodiments, the weight is based on the type of sensor each sensor of the plurality of sensor is. In some embodiments, the type of sensor is selected from an internal sensor or an external sensor. In some embodiments, an internal sensor has a higher weight than an external sensor. For example, an internal sensor may be assigned a weight of 10. In some embodiments, the weight of 10 indicates that the sensor performs without external signals. In some embodiments, an external sensor may be assigned a weight of 5. In some embodiments, the weight of 5 indicates that the sensor performs in response to external signals. The purpose of this is to indicate “Direct Trust” or sensors that can be trusted with high confidence with minimal evaluation of other factors vs “Indirect Trust” or sensors that can be trusted based on other factors.

[0040] In block 310C, a severity impact is determined for each sensor in the plurality of sensors. In some embodiments, the severity of impact is assigned a numerical value. For example, a value of 1 represents no impact, a value of 0.75 represents a minor impact, such as a minor inconvenience with useable performance degradation, a value of 0.5 represents a major impact, such as a core functionalist impacted with suboptimal performance degradation, and a value of 0 representing a critical impact, such as the sensor being inoperable. An example of an external factor that has a critical impact is radio frequency (RF) jamming on specific RF frequencies for a sensor that measures RF. An example of an external factor that has a maj or impact is wheel slippage (mud) for a sensor that measures odometry. An example of an external factor that has a minor impact is temperature variation for a sensor that measures inertia.

[0041] In block 315, an individual sensor performance is calculated for each sensor in the plurality of sensors. The individual sensor performance may be evaluated based on at least the metrics determined in step 310. Accordingly, the individual sensor performance may be based on at least the error variance of each sensor, the weight of each sensor, and the severity of performance impact due to one or more external factors. In some embodiments, the evaluation of the metrics is additive and then normalized. As such, each sensor is given a confidence score based on a combination of the estimated error variance, the number of external factors that may impact sensor performance, and each impact normalized. The higher the confidence score, the more likely it will be that the sensor will be trusted (i.e., the closer the confidence score will be to the predetermined confidence score).

[0042] There are external impacts that may negate the use of a sensor. For example, if RF jamming were to be present and realized by the system, sensors that use RF technologies within that frequency band would not be operational. As such, the observed impact to individual sensor performance caused by external factor is a multiplier to the final confidence score. In some embodiments, the individual sensor performance is calculated with Equation 1.

[0043] VNP Sv

[0044] Individual Sensor Performance = 0 * (E — T 4 — — )

[0045] Np

[0046] Equation 1

[0047] As shown in Equation 1, E is the estimated error variance. Npis the number of external factors that may impact sensor performance. Spis the severity of perfonnance impact due to one or more external factors. T is the type of sensor, and O is the observed impact to sensor performance caused by one or more external factors. In some embodiments, the confidence scoring process is done prior to the Kalman Filtering but is represented as a floating-point number in the sub-combination of sensor performances.

[0048] In block 320, a fused sensor performance is generated. In some embodiments, the Kalman Filter generates the fused sensor performance. In some embodiments, the fused sensor performance is generated by a controller (such as controller 104). In some embodiments, the fused sensor performance (also called the fused solution) is generated with Equation 2.

[0049] N

[0050] Fused Sensor Performance = Individual Sensor Performance k=0

[0051] Equation 2

[0052] As shown in Equation 2, N is the number of sensors.

[0053] In block 330, the plurality of sub-combination scores is compared with the fused sensor performance.

[0054] In block 335, a sensor performance is selected from the plurality of subcombination sensor performances or the fused sensor performance. The fused sensor performance may be chosen when its confidence score is closest to a predetermined confidence score where all sensors are operating without external factor impact (such as when all sensors have a severity impact of 1) or when one or more of the sensors are operating with only minor impact. If the observed impact to the individual sensor performance (wheel slip, RF Jamming, etc.) causes a sensor to become inoperable or severely impacted, then that fused sensor performance may not be utilized. Instead, the system may select a sub-combination sensor performance that is closer to the predetermined confidence score. In some embodiments, the system will select a subcombination sensor performance with the highest confidence score.

[0055] FIG. 4 is a block diagram of an example system 400, in accordance with the present technology. In some embodiments, the system 400 includes a plurality of sensors 402A, 402B, 402C. In some embodiments, the system 400 generates individual sensor performances for each sensor in the plurality of sensors 402A, 402B, 402C.

[0056] In blocks 405 A-l, 405 A-2, and 405 A-3, an error variance of each sensor of the plurality of sensors 402A, 402B, 402C is added to the individual sensor performances. In blocks 405B-1, 405B-2, and 405B-3, a severity impact is subtracted from the individual sensor performance for each sensor in a plurality of sensors 402A, 402B, 402C. In some embodiments, blocks 405A-1, 405A-2, and 405 A-3 and blocks 405B-1, 405B-2, and 405B- 3 occur simultaneously. In some embodiments, blocks 405A-1, 405A-2, and 405 A-3 and blocks 405B-1, 405B-2, and 405B-3 occur concurrently.

[0057] In blocks 410-1, 410-B, and 410-C, each sensor of the plurality of sensors 402A, 402B, 402C is weighted against the severity impact.

[0058] In some embodiments, the individual sensor performance is generated continuously. In such embodiments, as shown in blocks 415-1, 415-2, and 415-3 one or more external factors that impact the severity impact of each sensor in the plurality of sensors 402A, 402B, 402C, as described herein, are monitored. The monitoring of the one or more external sensors may occur continuously, so the severity impact is measured in real time. Accordingly, the individual sensor performance may be updated in real time based on the monitored external factors.

[0059] In blocks 420-1, 420-2, and 420-3, an individual sensor performance is output for each sensor performance. The individual sensor performance may be updated in real time based on the monitored external factors.

[0060] EXAMPLE

[0061] Notional examples of the fused solution and scoring metric through different environments are provided below. In this example, a fused sensor performance consists of measurements from three sensors: an Inertial Measurement Unit (1MU), a GPS receiver, and an odometer. The 1MU provides inertial acceleration and gyroscopic measurements, the GPS receiver provides position and velocity information, and the odometer provides speed measurements.

[0062] In this example the system 400 was used in three specific conditions: nominal GPS signal conditions, contested GPS conditions with marginal signal interference, and GPS denied conditions with critical signal interference.

[0063] Each sensor either provides an estimated one sigma variance, or the one sigma variance is internally estimated. In this example, the IMU, GPS receiver, and odometer have calculated variances of 5.0, 1.0, and 7.0 respectively through each environment. The IMU and odometer are internal sensors and have a sensor weight of 10, while the GPS receiver is an external sensor and has a sensor weight of 5. Further, the odometer and IMU are operating nominally and have no performance impacts due to external factors. The GPS RF environment affected performance and resulted in different severity impacts. For nominal GPS conditions, this value was 1. For contested GPS environments, this value was 0.5. For GPS denied environments, this value was 0.

[0064] The tactical grade IMU was impacted by external factors with associated severity impacts. In this example, the IMU was impacted by temperature: 0.75 (Minor); high vibration: 0.75 (Minor), and magnetic variation: 0.75 (Minor).

[0065] The GNSS receiver was also impacted by external factors with associated severity impacts. In this example, the GNSS receiver was impacted by electromagnetic interference: 0.5 (Major), and RF interference: 0 (Inoperable).

[0066] The wheel tick-based odometer was impacted by external factors with associated severity impacts. Here, the wheel tick-based odometer was impacted by high vibration: 0.75 (Minor), wheel slip: 0.5 (Major), and electromagnetic interference: 0.75 (Minor). This list is by no means comprehensive but rather an example of what may cause variation in the system.

[0067] An individual sensor performance was calculated for each sensor as shown below. In the below examples, Equation 1 was used to calculate each individual sensor performance.

[0068] In a nominal GPS environment, the calculations for each individual sensor performance are shown in Equations 3, 4, and 5.

[0069] IMU Sensor Performance = (1) * (5 — 10 + 0.75) = —5.75 Equation 3

[0070] Odometer Sensor Performance = (1) * (7 — 10 + .67) = —3.67

[0071] Equation 4

[0072] GPS Receiver Sensor Performance = (1) * (1 — 5 + .25) = —4.25 Equation 5

[0073] In a contested GPS environment, the calculations for each individual sensor performance are shown in Equations 6, 7, and 8.

[0074] IMU Sensor Performance = (1) * (5 — 10 + 0.75 ) = —5.75

[0075] Equation 6 Odometer Sensor Performance = (1) * (7 — 10 + 0.67) = —3.67

[0076] Equation 7

[0077] GPS Receiver Sensor Performance = (0.5) * (1 — 5 + 0.25) = —1.875

[0078] Equation 8

[0079] Finally, in a GPS denied environment, the calculations for each individual sensor performance are shown in Equations 9, 10, and 11.

[0080] IMU Sensor Performance = (1) * (5 — 10 + 0.75 ) = —5.75

[0081] Equation 9

[0082] Odometer Sensor Performance = (1) * (7 — 10 + 0.667) = —3.67

[0083] Equation 10

[0084] GPS Receiver Sensor Performance — (0) * (1 — 5 + 0.25) = 0

[0085] Equation 11

[0086] A plurality of sub-combination sensor performances was then generated for each situation, as shown below. The sub-combinations of sensor performances of the individual sensors were assessed based upon the individual sensor performance scores. Subcombinations of sensor performances are as follows: IMU and GPS, IMU and odometer, and GPS and odometer.

[0087] In the nominal GPS environment, the calculations for each sub-combination sensor performance are shown in Equations 12, 13, and 14.

[0088] IMU and GPS Receiver Performance = (—5.75 + —4.25) = — 10

[0089] Equation 12

[0090] IMU and Odometer Performance = (—5.75 + —3.67) = —9.42

[0091] Equation 13

[0092] GPS Reciever and Odometer Performance = (—4.25 + —3.67) = —7.92 Equation 14

[0093] The fused sensor performance was then calculated as the sum of all individual sensor performance metrics, using Equation 2, as shown in Equation 15.

[0094] Fused Performance — (—5.75 -I — 4.25 + —3.67) = —13.67

[0095] Equation 15

[0096] Because the fused sensor performance has the lowest score, and therefore the highest confidence score, in this example the fused sensor performance is selected as the solution.

[0097] In the contested GPS environment, the calculations for each sub-combination sensor performance are shown in Equations 16, 17, and 18.

[0098] IMU and GPS Reciever Performance = (—5.75 + —1.875) = —7.625

[0099] Equation 16

[0100] IMU and Odometer Performance = (—5.75 + —3.67) = —9.42

[0101] Equation 17

[0102] GPS Reciever and Odometer Performance = (—1.875 + —3.67) = —5.545 Equation 18

[0103] The fused sensor performance was then calculated as the sum of all individual sensor performance metrics, using Equation 2, as shown in Equation 19.

[0104] Fused Performance = (—5.75 H — 1.875 + —3.67) = —11.295

[0105] Equation 19

[0106] Here, the fused sensor performance still provides the lowest score, and therefore the highest confidence score, so the fused sensor performance is selected as the optimum solution.

[0107] In the GPS denied environment, the calculations for each sub-combination sensor performance are shown in Equations 20, 21, and 22.

[0108] 1MU and GPS Reciever Fused Performance = (—5.75 + 0) = —5.75

[0109] Equation 20

[0110] IMU and Odometer Fused Performance = (—5.75 + —3.67) = —9.42 Equation 21

[0111] GPS Reciever and Odometer Fused Performance = (—0 + —3.67) = —3.67 Equation 22

[0112] The fused sensor performance was then calculated as the sum of all individual sensor performance metrics, using Equation 2, as shown in Equation 23.

[0113] Fused Sensor Performance = (—5.75 + 0 + —3.67) = —9.42

[0114] Equation 23

[0115] Here, the fused sensor performance includes a sensor (the GPS Receiver) that has a critical external performance factor and is thus inoperable. The sub-combination of the IMU and the odometer has the lowest score (i.e., the highest confidence score). Accordingly, here, the sub-combination of the IMU and the odometer is selected as the solution.

[0116] The above description of illustrated examples of the invention, including what is described in the Abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. While specific examples of the invention are descnbed herein for illustrative purposes, various modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize.

[0117] These modifications can be made to the invention in light of the above detailed description. The terms used in the following claims should not be construed to limit the invention to the specific examples disclosed in the specification. Rather, the scope of the invention is to be determined entirely by the following claims, which are to be construed in accordance with established doctrines of claim interpretation.

Claims

CLAIMSWhat is claimed is:

1. A method of providing a position, navigation, or timing (PNT) solution, the method comprising: acquiring one or more measurements from a plurality of sensors attached to a platform, remote to the platform, or a combination thereof; determining an estimated error variance for each sensor of the plurality of sensors; determining a weight for each sensor of the plurality of sensors; determining a severity impact of each sensor of the plurality of sensors; calculating an individual sensor performance for each sensor of the plurality of sensors based on at least the estimated error variance, the weight, and the severity impact of each sensor in the plurality of sensors; generating a fused sensor performance by adding the individual sensor performance of each sensor of the plurality of sensors using a filter; generating a plurality of sub-combination sensor performances using the filter, wherein each sensor in a given sub-combination of sensor performances is characterized by its individual sensor performance, and wherein each sub-combination of sensor performances is determined by adding weighted individual sensor performances of the individual sensors of the given sub-combination; comparing the plurality of sub-combination sensor performances with the fused sensor performance, wherein each sub-combination sensor performance and the fused sensor performance have a confidence score based on the estimated error variance, the weight, and the severity impact of each sensor in the plurality of sensors; and selecting a sensor performance with a predetermined confidence score from the plurality of sub-combination sensor performances or the fused sensor performance.

2. The method of Claim 1 , wherein the predetermined confidence score is a highest confidence score from the plurality of sub-combination sensor performances or the fused sensor performance.

3. The method of Claim 1, wherein the fused sensor performance and the plurality of sub-combination performances are generated continuously, and wherein the filter is a single Karman Filter.

4. The method of Claim 3, wherein the method further comprises switching the sensor performance in real time, based on which sensor performance from the fused sensor performance or a sub-combination of the plurality of sub-combination performances has a highest confidence score.

5. The method of Claim 1, wherein the plurality of sensors includes an accelerometer, a gyroscope, an oscillator, a wheel tick sensor, an infrared sensor, an ultrasonic sensor, an optical sensor, a camera, a LiDAR sensor, a proximity sensor, a magnetometer, a barometer, a temperature sensor, a radio frequency sensor, or a humidity sensor.

6. The method of Claim 1, wherein the severity impact comprises: one or more external factors, and a severity of impact due to the one or more external factors.

7. The method of Claim 6, wherein the severity impact is adjusted based on observation of the one or more external factors.

8. The method of Claim 6, wherein the one or more external factors are selected from RF jamming, spoofing, visible degradation, destruction of a sensor, temperature, wheel slippage, or a combination thereof.

9. The method of Claim 6, wherein the individual sensor performance is determined by:wherein E is the estimated error variance;Np is a number of the one or more external factors;Sp is the severity of impact due to the one or more external factors;T is a type of sensor; andO is an observed impact caused by the one or more external factors.

10. The method of Claim 9, wherein the type of sensor is selected from an internal sensor or an external sensor.

11. The method of Claim 10, wherein the internal sensor has a higher weight than the external sensor.

12. A system of generating a position, navigation, or timing (PNT) solution, the system comprising: a plurality of sensors configured to take one or more measurements; and a controller configured for: receiving one or more measurements from the plurality of sensors; determining an estimated error variance for each sensor of the plurality of sensors; determining a weight for each sensor of the plurality of sensors; determining a severity impact of each sensor of the plurality of sensors; calculating an individual sensor performance for each sensor of the plurality of sensors based on at least the estimated error variance, the weight, and the severity impact of each sensor in the plurality of sensors; generating a fused sensor performance by adding the individual sensor performance of each sensor of the plurality of sensors using a filter; generating a plurality of sub-combination sensor performances using the filter, wherein each sensor in a sub-combmation of sensor performances is characterized by its individual sensor performance, and wherein each sub-combination of sensor performances is determined by adding weighted individual sensor performances of the individual sensors belonging to each sub-combination of sensor performances of the given sub-combination; comparing the plurality of sub-combination sensor performances with the fused sensor performance, wherein each sub-combination sensor performance and the fused sensor performance have a confidence score based on the estimated error variance, the weight, and the severity impact of each sensor in the plurality of sensors; and selecting a sensor performance of the plurality of sub-combination sensor performances or the fused sensor performance with a predetermined confidence score.

13. The system of Claim 12, wherein the controller is further configured to generate the fused sensor performance score and a plurality of sub-combination scores continuously, and wherein the filter is a single Karman Filter.

14. The system of Claim 13, wherein the controller is further configured to switch the sensor performance in real time, based on which sensor performance from the fused sensor performance or a sub-combination of the plurality of sub-combination performances has a highest confidence score.

15. The system of Claim 12, wherein the plurality of sensors includes an accelerometer, a gyroscope, an oscillator, a wheel tick sensor, an infrared sensor, an ultrasonic sensor, an optical sensor, a camera, a LiDAR sensor, a proximity sensor, a magnetometer, a barometer, a temperature sensor, a radio frequency sensor, or a humidity sensor.

16. The system of Claim 12, wherein the severity impact comprises: one or more external factors; and a severity of impact due to the one or more external factors.

17. The system of Claim 16, wherein the severity impact is adjusted based on observation of the one or more external factors.

18. The system of Claim 16, wherein the one or more external factors are selected from RF jamming, spoofing, visible degradation, destruction of a sensor, temperature, wheel slippage, vibration, magnetic variation, electromagnetic interference, or a combination thereof.

19. The system of Claim 16, wherein the individual sensor performance is detemiined by:wherein E is the estimated error variance;Np is a number of the one or more external factors that impact the individual sensor performance;Spis a severity impact due to the one or more external factors that may impact individual sensor performance;T is a type of sensor; andO is an observed impact caused by the one or more external factors.

20. The system of Claim 19, wherein the type of sensor is selected from an internal sensor or an external sensor.

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