Matched field processing (MFP) has traditionally been considered as an estimation problem in which the goal is to use the observed pressure field and an acoustic propagation model to estimate an unknown source location with the smallest possible variance. An alternative perspective is to divide the search region into cells whose size is dictated by logistical constraints, and attempt to assign the source to one of these cells. In this perspective, the goal of MFP is to minimize the probability of error in assigning the source to a grid cell, rather than minimizing the variance of the source location estimate. This perspective leads one to consider MFP as a communication problem in which the source transmits its location to the receiver array. Information theory provides techniques which can bound the amount of information available to the receiver in this problem. The amount of information observable has implications for the performance limits achievable by any MFP algorithm. Additionally, this perspective leads to methods of designing arrays for MFP which maximize the average information observed over the search space. [Work supported by ONR Young Investigator Program.]
- Information theory for matched field processing: preliminary results
- John R. Buck - University of Massachusetts Dartmouth
- The Journal of the Acoustical Society of America, Vol.109(5_Supplement), pp.2319-2319
- 1
- English
- Department of Electrical and Computer Engineering
- Conference proceeding
- 9914419793701301