Combining redundant inertial sensors to create a virtual sensor output

Abstract

Included are embodiments for determining an inertial quantity. One embodiment of a method includes combining readings from a plurality of inertial sensors to produce an estimate of the value of an inertial quantity in a manner that is fault-tolerant, more accurate than traditional sensor arrangements, and able to handle non-linear and non-Gaussian systems. Embodiments of a method also include utilizing a Monte Carlo estimation-based inference system to adaptively combine the inertial sensor outputs into a fault-tolerant highly-accurate inertial quantity estimate, an axis-reversed-paired physical arrangement of inertial sensors to minimize effects of environmental and process noise, and cross-associating sensors to ensure good sensor associations and reduce the effects of sample impoverishment.

1 . A method of determining an inertial quantity in a system with linear, non-linear, Gaussian, or non-Gaussian characteristics or combinations thereof, the method comprising:
 associating a plurality of inertial sensor devices with the system;   acquiring inertial sensor data form the plurality of inertial sensor devices;   utilizing a computing device for processing the inertial sensor data by application of a Monte Carlo estimation-based inference system configured to produce estimates in systems with linear, non-linear, Gaussian, and non-Gaussian characteristics, wherein the computing device produces a virtual connection between at least two of the plurality of inertial sensor devices, resulting in a virtual sensor output value, the virtual sensor output value being of a degree of accuracy exceeding that which would otherwise be attributable to one of the single inertial sensor devices; and   utilizing the virtual sensor output value as at least part of a tangible indication of the inertial quantity.   
     
     
         2 . The method of  claim 1 , wherein the Monte Carlo estimation-based inference system comprises an Unscented Particle Filter, incorporating an Unscented Kalman Filter to perform predictions of time updates and measurement updates for each particle. 
     
     
         3 . The method of  claim 1 , wherein the computing device comprises a firmware controlled digital processor, a programmed portion of a digital software controlled computer, a logic circuit, or an application-specific integrated circuit for performing the recited function. 
     
     
         4 . The method of  claim 1 , wherein the Monte Carlo estimation-based inference system is an Unscented Particle Filter, configured to perform the following:
 read initial inertial sensor data from the plurality of inertial sensor devices;   establish an initial estimation of the initial inertial sensor data;   create a set of particles and establishing a position for each particle in the set of particles;   initialize a covariance matrix for an Unscented Kalman Filter for each particle;   execute an Unscented Particle Filter update process at least one time, comprising:   read current iteration of inertial sensor data from the plurality of inertial sensor devices;   scatter particle positions for each particle by adding noise to them according to a probability density function which may be determined experimentally or may be based on noise characteristics of the inertial sensor devices being used;   use an Unscented Kalman Filter to update the position for each particle further by way of prediction;   perform an importance sampling by assigning a weight to each particle based on the probability of the current iteration of inertial sensor data given that this particle's position is treated as a sensor data element;   normalize the importance weights of all particles with respect to the total of all particle importance weights:   apply selective-importance-resampling to the particles to adjust each particle's importance weighting by comparing each particle to the set of inertial sensor data obtained from the plurality of inertial sensor devices;   generate the virtual sensor output value using an importance weighted sum of the particles; and   output the virtual sensor output value as a tangible indication of the inertial quantity.   
     
     
         5 . The method of  claim 1 , in which the Monte Carlo estimation-based inference system is a Particle Filter, Sampling Importance Resampling Filter, Auxiliary Particle Filter, Extended Kalman Particle Filter, Condensation Particle Filter, Ensemble Kalman Filter, or Markov Chain Monte Carlo process. 
     
     
         6 . The method of  claim 1 , in which the Monte Carlo estimation-based inference system is dynamically selected from a set of Monte Carlo estimation-based inference systems based on a set of operating and performance characteristic criteria, such as algorithmic execution time or accuracy. 
     
     
         7 . An apparatus determining an inertial quantity of a system, comprising:
 a substrate;   a first inertial sensor device that comprises a first axis with a corresponding first direction of sensitivity, the first inertial sensor device being coupled to the substrate;   a second inertial sensor device that comprises a second axis with a corresponding second direction of sensitivity, the second inertial sensor device being coupled to the substrate, wherein the second direction of sensitivity is inverted relative to the first direction of sensitivity;   a computing device for processing inertial sensor data from the first inertial sensor device, and inertial sensor data from the second inertial sensor device;   generating a virtual sensor output value from the inertial sensor data from the first inertial sensor device and the inertial sensor data from the second inertial sensor device; and   utilizing the virtual sensor output value as at least part of a tangible indication of the inertial quantity.   
     
     
         8 . The apparatus of  claim 7 , wherein the computing device comprises a firmware controlled digital processor, a programmed portion of a digital software controlled computer, a logic circuit, an application-specific integrated circuit, or an analog circuit, for performing the recited function. 
     
     
         9 . A method of creating a plurality of virtual inertial sensors for determining an inertial quantity of a system, comprising:
 associating a plurality of inertial sensor devices with the system;   acquiring inertial sensor data from a set of N inertial sensor devices, where N is a number of inertial sensor devices being measured;   combining by a computing device, data from a first inertial sensor device, chosen from the set of N inertial sensor devices, with data from a second inertial sensor device, chosen from the set of N inertial sensor devices;   generating a virtual sensor output value from the inertial sensor data from the first inertial sensor device and the inertial sensor data from the second inertial sensor device; and   utilizing the virtual sensor output value as at least part of a tangible indication of the inertial quantity.   
     
     
         10 . The method of  claim 9 , in which inertial sensor data from a first set of one or more inertial sensor devices, chosen from the set of N inertial sensor devices, is combined with inertial sensor data from a second set of one or more inertial sensor devices, chosen from the set of N inertial sensor devices. 
     
     
         11 . The method of  claim 9 , in which the virtual sensor output values generated by the computing device are dynamically generated based on a set of operating and performance characteristic criteria, such as algorithmic execution time or accuracy. 
     
     
         12 . The apparatus of  claim 9 , wherein the computing device comprises a firmware controlled digital processor, a programmed portion of a digital software controlled computer, a logic circuit, an application-specific integrated circuit, or an analog circuit, for performing the recited function.