A. Satish et Rl. Kashyap, MULTIPLE-TARGET TRACKING USING MAXIMUM-LIKELIHOOD PRINCIPLE, IEEE transactions on signal processing, 43(7), 1995, pp. 1677-1695
We propose a method (tracking algorithm (TAL)) based on the maximum li
kelihood (ML) principle for multiple target tracking in near-field usi
ng outputs from a large uniform linear array of passive; sensors, The
targets are assumed to be narrowband signals and modeled as sample fun
ctions of a Gaussian stochastic process, The phase delays of these sig
nals are expressed as functions of both range and bearing angle (''tra
ck parameters'') of respective targets, A new simplified likelihood fu
nction for ML estimation of these parameters is derived from a second-
order approximation on the inverse of the data covariance matrix, Maxi
mization of this likelihood function does not involve inversion of the
M x M data covariance matrix, where M denotes number of sensors in th
e array, Instead, inversion of only a D x D matrix is required, where
D denotes number of targets, In practice, D much less than M and, henc
e, TAL is computationally efficient, Tracking is achieved by estimatin
g track parameters at regular time intervals wherein targets move to n
ew positions in the neighborhood of their previous positions, TAL pres
erves ordering of track parameter estimates of the D targets over diff
erent time intervals. Performance results of TAL are presented, and it
is also compared with methods in papers by Sword and by Swindlehurt a
nd Kailath. Almost exact asymptotic expressions for the Cramer-Rao bou
nd (CRB) on the variance of angle and range estimates are derived, and
their utility is discussed.