Efficient configuration calibration using ground auxiliary receivers at inaccurate locations

Identifiability Analysis for Configuration Calibration in Distributed Sensor Networks

“…In ( 11) and ( 12), K 1 and K 2 represent constants irrelevant of η, µ represents the mean vector of r with covariance matrix Σ r , and Σ η defines the covariance matrix of η. Inserting (11) and ( 12) into (10), applying partial derivatives w.r.t. the unknowns in η twice, negating the sign, and taking expectation in the sequel yield the BFIM as [5] J(η) = E η [J D (η)] + J P (η) (13) where J D (η) and J P (η) are components of J(η) describing, respectively, contributions of the measurements and prior statistics to the lower bound on the estimation error. For ease of description, we hereafter refer to J D (η) as measurable FIM and J P (η) as prior FIM.…”

“…An earlier series of papers realized the problem of uncertainty in array shape [4][5][6][7][8][9][10][11][12][13][14]. It is straightforward to equip each sensor with a global positioning system (GPS) to obtain its position information, but this adds to the expense and power requirement of the sensor and increases its susceptibility to detection [4].…”

“…It is straightforward to equip each sensor with a global positioning system (GPS) to obtain its position information, but this adds to the expense and power requirement of the sensor and increases its susceptibility to detection [4]. Signal coherence and accurate source localization demand a centimeter-level sensor positioning accuracy which cannot be provided by the GPS, for the GPS only achieves a meter-level positioning accuracy with the current state of art [5]. In [6], it is reported that if all sensor positions are unknown, inter-sensor measurements only produce relative sensor positions with translation and rotation uncertainties.…”