TY - GEN
T1 - Power allocation schemes for target localization in widely distributed MIMO radar systems
AU - Godrich, Hana
AU - Petropulu, Athina
AU - Poor, H. Vincent
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Widely distributed multiple-input multiple-output (MIMO) radar systems offer parameter estimation improvement for target localization, proportional to the product of the number of transmitting and receiving radars and the total transmitted power. Thus far, power allocation has been uniformly distributed between the system transmitters. For a large number of radars, the achievable localization mean-square error (MSE), with full resource allocation, may extend beyond the system predetermined performance goals, such as target localization accuracy (i.e., minimum estimation MSE) and total power radiation. In this study, power allocation schemes are developed, taking into account system constraints. The first is concerned with minimizing the total transmitted power such that a predefined estimation MSE objective is met, while keeping the transmitted power at each station within an acceptable range. The second, optimally distributes a given power budget among all transmitting radars to maximize performance, i.e., minimize the attainable localization MSE. As the Cramer-Rao bound (CRB) is known to be asymptotically tight to the maximum likelihood estimator (MLE) MSE at high SNR, it is used as a metric for the estimation MSE. The CRB is derived for a signal model that incorporates the propagation path loss, the target radar cross section (RCS), and the transmitters' powers. It is shown that uniform or equal power allocation is not in general optimal and that the proposed allocation algorithms result in a local optimum that provided either better localization MSE for the same power budget or requires less power to establish the same performance in terms of estimation MSE. A physical interpretation of these conclusions is offered.
AB - Widely distributed multiple-input multiple-output (MIMO) radar systems offer parameter estimation improvement for target localization, proportional to the product of the number of transmitting and receiving radars and the total transmitted power. Thus far, power allocation has been uniformly distributed between the system transmitters. For a large number of radars, the achievable localization mean-square error (MSE), with full resource allocation, may extend beyond the system predetermined performance goals, such as target localization accuracy (i.e., minimum estimation MSE) and total power radiation. In this study, power allocation schemes are developed, taking into account system constraints. The first is concerned with minimizing the total transmitted power such that a predefined estimation MSE objective is met, while keeping the transmitted power at each station within an acceptable range. The second, optimally distributes a given power budget among all transmitting radars to maximize performance, i.e., minimize the attainable localization MSE. As the Cramer-Rao bound (CRB) is known to be asymptotically tight to the maximum likelihood estimator (MLE) MSE at high SNR, it is used as a metric for the estimation MSE. The CRB is derived for a signal model that incorporates the propagation path loss, the target radar cross section (RCS), and the transmitters' powers. It is shown that uniform or equal power allocation is not in general optimal and that the proposed allocation algorithms result in a local optimum that provided either better localization MSE for the same power budget or requires less power to establish the same performance in terms of estimation MSE. A physical interpretation of these conclusions is offered.
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U2 - 10.1109/MILCOM.2010.5680193
DO - 10.1109/MILCOM.2010.5680193
M3 - Conference contribution
AN - SCOPUS:79951656460
SN - 9781424481804
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 846
EP - 851
BT - 2010 IEEE Military Communications Conference, MILCOM 2010
T2 - 2010 IEEE Military Communications Conference, MILCOM 2010
Y2 - 31 October 2010 through 3 November 2010
ER -