Sensor array calibration for uniform rectangular array in presence of mutual coupling and sensor gain-and-phase errors

The sensor array calibration methods tailored to uniform rectangular array(URA)in the presence of mutual coupling and sensor gain-and-phase errors were addressed.First,the mutual coupling model of the URA was studied,and then a set of steering vectors corresponding to distinct locations were numerically computed with the help of several time-disjoint auxiliary sources with known directions.Then,the optimization modeling with respect to the array error matrix(defined by the product of mutual coupling matrix and sensor gain-and-phase errors matrix)was constructed.Two preferable algorithms(called algorithm I and algorithm II)were developed to minimize the cost function.In algorithm I,the array error matrix was regarded as a whole parameter to be estimated,and the exact solution was available.Compared to some existing algorithms with the similar computation framework,algorithm I can make full use of the potentially linear characteristics of URA's error matrix,thus,the calibration precision was obviously enhanced.In algorithm II,the array error matrix was decomposed into two matrix parameters to be optimized.Compared to algorithm I,it can further decrease the number of unknowns and,thereby,yield better estimation accuracy.However,algorithm II was incapable of producing the closed-form solution and the iteration operation was unavoidable.Simulation results validate the excellent performances of the two novel algorithms compared to some existing calibration algorithms.

Efficient closed-form solution for target localization using TDOA measurements in the presence of sensor position errors

An efficient solution for locating a target was proposed, which by using time difference of arrival （TDOA） measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares （TSWLS） method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares （WLS）. The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound （CRLB） easily and effectively improve the localization accuracy.

Accelerometer Bias and Star Sensor Installation Error Joint Calibration for the SINS/CNS Integrated Navigation System Using Attitude Maneuver

The integrated strap-down inertial nav igation system/olelestial navigation system(SINS/CNS)i an important autonomous navigation method with efective concealment and high predision.Both accelerometer biss and star ensor installation error ame important factors that aflect the performanoe of this mavigation system,which needl to be calibratexd and compensatedl.A new acelerometer bias and star sensor installation error joint calibration method for the SINS/CNS integrated navigation system i propoeed.In this newly propoeed method,the installation error of star sensor is augmented to the state vector,and the star vector,nadir angle,horkzontal poeition error and velbcity error ame ueed a8 measurementa to calbrate the two errors mentioned above.Simulations show that both accelerometer bias and star sensor installation enror an be calibratedl efectively.