Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Classical and Modern Direction-of-Arrival Estimation
Classical and Modern Direction-of-Arrival Estimation
Maximum-likelihood direction-of-arrival estimation in the presenceof unknown nonuniform noise
IEEE Transactions on Signal Processing
Parametric sensor array calibration using measured steering vectorsof uncertain locations
IEEE Transactions on Signal Processing
Applications of cumulants to array processing. IV. Directionfinding in coherent signals case
IEEE Transactions on Signal Processing
Applications of cumulants to array processing .I. Apertureextension and array calibration
IEEE Transactions on Signal Processing
On the virtual array concept for higher order array processing
IEEE Transactions on Signal Processing
Stochastic Crame´r-Rao bound for noncircular signals with application to DOA estimation
IEEE Transactions on Signal Processing
Asymptotic performance analysis of ESPRIT, higher order ESPRIT, andvirtual ESPRIT algorithms
IEEE Transactions on Signal Processing
Second-order complex random vectors and normal distributions
IEEE Transactions on Signal Processing
Passive Source Localization Using an Airborne Sensor Array in the Presence of Manifold Perturbations
IEEE Transactions on Signal Processing
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A new method for joint direction-of-arrival (DOA) and sensor position estimation is introduced. The sensors are assumed to be randomly deployed except two reference sensors. The proposed method exploits the advantages of both higher-order-statistics (HOS) and second-order-statistics (SOS) with an iterative algorithm, namely Iterative Higher-Order Second-Order Statistics (IHOSS). A new cumulant matrix estimation technique is proposed for the HOS approach by converting the multisource problem into a single source one. IHOSS performs well even in case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. A cost function is defined for the joint DOA and position estimation. The iterative procedure is guaranteed to converge. The ambiguity problem in sensor position estimation is solved by observing the source signals at least in two different frequencies. The conditions on these frequencies are presented. Closed-form expressions are derived for the deterministic Cramér-Rao bound (CRB) for DOA and unknown sensor positions for noncircular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performance of IHOSS is significantly better than the HOS approaches for DOA estimation and closely follows the CRB for both DOA and sensor position estimations.