Phase wrap error correction by random sample consensus with application to synthetic aperture sonar micro-navigation

Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270&amp;#x2013;330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29&amp;#x0025;&amp;#x2013;125&amp;#x0025; and 30&amp;#x0025;&amp;#x2013;150&amp;#x0025;, respectively, compared to 16&amp;#x0025;&amp;#x2013;134&amp;#x0025; for the branch-cuts method for this data set.</p

Soft biometrics systems: Reliability and asymptotic bounds

Abstract—This work presents a preliminary statistical analysis on the reliability of soft biometrics systems which employ multiple traits for human identification. The analysis places emphasis on the setting where identification errors occur mainly due to cross-subject interference, i.e., due to the event that subjects share similar facial and body characteristics. Finally asymptotic anal-ysis provides bounds which insightfully interpret this statistical behavior. I

Model-based 3D micro-navigation and bathymetry estimation for interferometric synthetic aperture sonar

Sub-wavelength navigation information is vital for the formation of all synthetic aperture sonar (SAS) data products. This challenging requirement can be achieved using the redundant phase centre (RPC) or displaced phase centre antenna (DPCA) micro-navigation algorithm, which uses cross-correlation of signals with inter-ping coherence to estimate time delays and hence make navigation estimates. In this paper a new approach to micro- navigation for interferometric synthetic aperture sonar is introduced. The algorithm makes 3D vehicle position estimates for each sonar ping by making use of time delays measured between all possible pairs of redundant phase centre arrays, using both interferometric arrays on each side of the vehicle. Simultaneous estimation of coarse bathymetry allows the SAS images to be projected onto ground-range. The method is based on non-linear minimization of the difference in modelled and measured time delays and surges between redundant phase centre arrays. The approach is demonstrated using data collected by the CMRE MUSCLE AUV using its 270-330 kHz SAS during the MANEX’14 experiment. SAS images have been projected onto the coarsely estimated bathymetry, and interferograms have been formed. The coarse bathymetry estimate and vehicle navigation estimate are validated by the quality of the image focussing and the near-zero phase of the interferogram. The method has the potential to improve through-the-sensor navigation aiding and to increase the accuracy of single-pass bathymetry estimation. Future development of the algorithm for repeat-pass operation has the potential to enable repeat-pass track registration in three dimensions. The method is therefore an important step towards improved coherent change detection and high resolution bathymetry estimation

Phase wrap error correction by random sample consensus with application to synthetic aperture sonar micro-navigation

Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270&amp;#x2013;330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29&amp;#x0025;&amp;#x2013;125&amp;#x0025; and 30&amp;#x0025;&amp;#x2013;150&amp;#x0025;, respectively, compared to 16&amp;#x0025;&amp;#x2013;134&amp;#x0025; for the branch-cuts method for this data set.</p