An efficient Newton-type method for the computation of ML estimators in a uniform linear array

A design study on complexity reduced multipath mitigation

“…(5), Selva (2004Selva ( , 2005 developed the basic CRMM algorithm, which implements the following two steps:…”

“…Thus, the positioning accuracy of state-of-the art mass market GNSS receivers with NEML DLLs will be degraded from a few meters to several tens of meters. In van Nee et al (1994), maximum likelihood (ML) multipath estimation (ME) algorithms can mitigate these errors but are computationally too expensive for mass market GNSS receivers in terms of computing power and power consumption. Consequently, Selva (2004Selva ( , 2005 developed complexity reduced multipath mitigation (CRMM) algorithms that employ a bank of correlators to reduce the amount of data that needs to be handled by the MLME.…”

Efficient Implementation of the Capon Beamforming Using the Levenberg-Marquardt Scheme for Two Dimensional Aoa Estimation

“…Angleof-arrival (AOA) estimation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] and beamforming [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] have been main research topics of the array signal processing. Note that array signal processing can also be applied to radar signal processing [39][40][41][42][43].…”

“…For N incident signals, the cost function of the maximum likelihood (ML) algorithm [2,3] is pN -dimensional, where p is the number of parameters to be estimated for each incident signal. For the estimation of azimuth angle, p = 1, the cost function is N -dimensional.…”

“…Therefore, for large value of N , the optimization of pN -dimensional cost function by exhaustive grid search is not feasible. That is why Newton-type algorithms including the LM algorithm have been widely applied to the optimization of the cost function of the ML algorithm [2,3].…”