Error and performance analysis of cold-atom inertial sensors for navigation
Error and performance analysis of cold-atom inertial sensors for navigation
 
  Cold-atom inertial (CAI) sensors based on light-pulse atom interferometry show much promise for the next generation of navigation systems thanks to their low scale-factor and bias instability. Despite the high performance demonstrated in laboratory-based experiments, CAI technology is still far from being deployed in real-world navigation applications, and an analysis of the potential errors must be carried out in order to assess their impact on sensor performance. Within this context, we conduct a theoretical analysis to identify some of the most important error sources, disclose their physical mechanisms, and assess their impact on sensor performance. Through a multidisciplinary approach that combines different methodologies spanning from system engineering to quantum physics modeling, we analyse the response of the CAI sensor to several error sources, including scale-factor, bias, and noise, and establishing clear relations between system parameters and sensor performance. Particular emphasis is given to error sources stemming from the laseratom interaction during the Raman-pulse sequence and from state detection and imaging. Moreover, we present a method for optimizing beam-splitter pulses based on timedependent perturbation theory, demonstrating improvements in the simulated performance of a CAI sensor. Finally. due to its attractive multi-axial sensitivity and inherent capability to discriminate the acceleration from the rotational signal, we study in more detail errors sources in CAI sensors based on point-source interferometry. We therefore present a read-out method based on Kalman filtering to extract the interferometric phase map from interferograms, along with a compensation scheme for real-time calibration of the rotational scale-factor based on the integration of the CAI sensor with classical inertial sensors
  Cold-atom inertial sensor, Inertial navigation, Error analysis, Modelling atom interferometry
  
    University of Southampton
   
  
    
      Dedes, Nikolaos
      
        aa6b8f4d-bd3a-4b1c-834d-14126ddba38f
      
     
  
  
   
  
  
    
      October 2024
    
    
  
  
    
      Dedes, Nikolaos
      
        aa6b8f4d-bd3a-4b1c-834d-14126ddba38f
      
     
  
    
      Freegarde, Tim
      
        01a5f53b-d406-44fb-a166-d8da9128ea7d
      
     
  
    
      Gates, James
      
        b71e31a1-8caa-477e-8556-b64f6cae0dc2
      
     
  
       
    
 
  
    
      
  
 
  
  
  
    Dedes, Nikolaos
  
  
  
  
   
    (2024)
  
  
    
    Error and performance analysis of cold-atom inertial sensors for navigation.
  University of Southampton, Doctoral Thesis, 248pp.
  
   
  
    
      Record type:
      Thesis
      
      
      (Doctoral)
    
   
    
    
      
        
          Abstract
          Cold-atom inertial (CAI) sensors based on light-pulse atom interferometry show much promise for the next generation of navigation systems thanks to their low scale-factor and bias instability. Despite the high performance demonstrated in laboratory-based experiments, CAI technology is still far from being deployed in real-world navigation applications, and an analysis of the potential errors must be carried out in order to assess their impact on sensor performance. Within this context, we conduct a theoretical analysis to identify some of the most important error sources, disclose their physical mechanisms, and assess their impact on sensor performance. Through a multidisciplinary approach that combines different methodologies spanning from system engineering to quantum physics modeling, we analyse the response of the CAI sensor to several error sources, including scale-factor, bias, and noise, and establishing clear relations between system parameters and sensor performance. Particular emphasis is given to error sources stemming from the laseratom interaction during the Raman-pulse sequence and from state detection and imaging. Moreover, we present a method for optimizing beam-splitter pulses based on timedependent perturbation theory, demonstrating improvements in the simulated performance of a CAI sensor. Finally. due to its attractive multi-axial sensitivity and inherent capability to discriminate the acceleration from the rotational signal, we study in more detail errors sources in CAI sensors based on point-source interferometry. We therefore present a read-out method based on Kalman filtering to extract the interferometric phase map from interferograms, along with a compensation scheme for real-time calibration of the rotational scale-factor based on the integration of the CAI sensor with classical inertial sensors
         
      
      
        
          
            
  
    Text
 PhD_Thesis_Final
     - Version of Record
   
  
  
    
  
 
          
            
          
            
           
            
           
        
          
            
  
    Text
 Final-thesis-submission-Examination-Mr-Nikolaos-Dedes (1)
    
   
  
    
      Restricted to Repository staff only
    
  
  
 
          
            
           
            
           
        
        
       
    
   
  
  
  More information
  
    
      Published date: October 2024
 
    
  
  
    
  
    
  
    
  
    
  
    
     
    
  
    
     
        Keywords:
        Cold-atom inertial sensor, Inertial navigation, Error analysis, Modelling atom interferometry
      
    
  
    
  
    
  
  
        Identifiers
        Local EPrints ID: 494819
        URI: http://eprints.soton.ac.uk/id/eprint/494819
        
        
        
        
          PURE UUID: cfd908a7-af52-472c-83f4-2cd3ca4eeccf
        
  
    
        
          
            
          
        
    
        
          
            
              
            
          
        
    
        
          
            
              
            
          
        
    
  
  Catalogue record
  Date deposited: 16 Oct 2024 16:38
  Last modified: 10 Jan 2025 02:41
  Export record
  
  
 
 
  
    
    
      Contributors
      
        
      
        
      
          
          Thesis advisor:
          
            
              
              
                James Gates
              
              
                 
              
            
            
          
         
      
      
      
    
  
   
  
    Download statistics
    
      Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
      
      View more statistics