| Sensor arrays are used in a number of operational systems, including those designed for air surveillance, underwater target detection and localization, communications, radio astronomy and medical imaging and may also soon manifest themselves in multiple-platform clusters and satellite clouds. As the number of applications increases and the processing techniques become more accurate, the motion of the array becomes of increasing concern. These time-varying arrays cannot use the same algorithms that time-invariant arrays use because they are designed for stationary random processes and the output of the time-varying array is nonstationary. In this work we discuss several novel beamforming, direction-of-arrival estimation, and spatial spectrum estimation algorithms for time-varying arrays of sensors. Key to all of these applications is estimation of the covariance matrix, for if that is known existing algorithms that make use of the covariance matrix may be applied. Since the covariance matrix associated with the nonstationary data stream is also time-variant we have designed an algorithm to estimate a sequence of covariance matrices. We also discuss a modification we have made to the MUSIC algorithm that makes use of the resulting matrix sequence and offers improved performance in direction-of-arrival estimation. Similarly we introduce a modified MVDR spatial spectrum estimation algorithm which also uses the covariance matrix sequence. It is shown through these algorithms that array motion can be an asset rather than a liability. We have simulated data from a rotating uniform linear array and present the results of the algorithms' application to it. |
