TY - JOUR
T1 - Improved Sequential Music
AU - Stoica, Petre
AU - Handel, Peter
AU - Nehorai, Arye
N1 - Funding Information:
The work of P. Stoica was supported in part by the Swedish Research Council of Engineering Sciences under Contract 91-676. The work of A. Nehorai was supported by the Air Force Office of Scientific Research, under Grant AFOSR-90-0164, the Office of Naval Research under Grant N00014-91-J-1298, and the National Science Foundation under Grant MIP-9122753.
PY - 1995/10
Y1 - 1995/10
N2 - MUSIC (multiple signal classification) is one of the most frequently considered methods for source location using sensor arrays. Among the location methods based on one-dimensional search, MUSIC has excellent performance. In fact, no other one-dimensional method that may outperform MUSIC (in large samples) was known to exist. Our goal here is to introduce such a method, called improved sequential MUSIC (IES-MUSIC), which is shown to be strictly more accurate than MUSIC (in large samples). First, a class of sequential MUSIC estimates is introduced, which depend on a scalar-valued user parameter. MUSIC is shown to be a special case of estimate in that class, corresponding to a value of zero for the user parameter. Next, the optimal user parameter value, which minimizes the asymptotic variance of the estimation errors, is derived. IES-MUSIC is the method based on that optimal choice of the user parameter. Simulation results which lend support to the theoretical findings are included.
AB - MUSIC (multiple signal classification) is one of the most frequently considered methods for source location using sensor arrays. Among the location methods based on one-dimensional search, MUSIC has excellent performance. In fact, no other one-dimensional method that may outperform MUSIC (in large samples) was known to exist. Our goal here is to introduce such a method, called improved sequential MUSIC (IES-MUSIC), which is shown to be strictly more accurate than MUSIC (in large samples). First, a class of sequential MUSIC estimates is introduced, which depend on a scalar-valued user parameter. MUSIC is shown to be a special case of estimate in that class, corresponding to a value of zero for the user parameter. Next, the optimal user parameter value, which minimizes the asymptotic variance of the estimation errors, is derived. IES-MUSIC is the method based on that optimal choice of the user parameter. Simulation results which lend support to the theoretical findings are included.
UR - https://www.scopus.com/pages/publications/0029394185
U2 - 10.1109/7.464347
DO - 10.1109/7.464347
M3 - Article
AN - SCOPUS:0029394185
SN - 0018-9251
VL - 31
SP - 1230
EP - 1239
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 4
ER -