PROJECT TITLE :

Performance Analysis of Coarray-Based MUSIC in the Presence of Sensor Location Errors - 2018

ABSTRACT:

Sparse linear arrays, such as co-prime and nested arrays, will resolve a lot of uncorrelated sources than the number of sensors by applying the MUtiple SIgnal Classification (MUSIC) algorithm to their difference coarray model. We have a tendency to aim at statistically analyzing the performance of the MUSIC algorithm applied to the difference coarray model, namely, the coarray-primarily based MUSIC, within the presence of sensor location errors. We have a tendency to 1st introduce an indication model for sparse linear arrays within the presence of deterministic unknown location errors. Based mostly on this signal model, we tend to derive a closed-type expression of the asymptotic mean-squared error of a commonly used coarray-based MUSIC algorithm, SS-MUSIC, within the presence of tiny sensor location errors. We have a tendency to show that the sensor location errors introduce a relentless bias that depends on each the physical array geometry and the coarray geometry, which can't be mitigated by solely increasing the signal-to-noise ratio. We have a tendency to next offer a brief extension of our analysis to cases when the sensor location errors are stochastic and investigate the Gaussian case. Finally, we have a tendency to derive the Cramér-Rao certain for joint estimation of direction-of-arrivals and sensor location errors for sparse linear arrays, that will be applicable even if the amount of sources exceeds the amount of sensors. Numerical simulations show sensible agreement between empirical results and our theoretical results.


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