For mobile radars installed on a gyro-stabilised platform (GSP) that can steadily follow an East-North-Up (ENU) frame, attitude biases (ABs) of the platform and offset biases (OBs) of the radar are linear dependent variables. Therefore ABs and OBs are unobservable in the linearized registration equations; however, when combining them as new variables, the system becomes observable, and this model has been called the unified registration model (URM). Unlike GSP mobile radars, un-stabilised GSP (or UGSP) mobile radars are installed on the platform directly and rotate with the platform simultaneously. For UGSP, it is testified that both types of biases are independent and observable because the time-varying attitude angles (AAs)1 of the platform are included in the registration equations, which destroy the dependencies of both kinds of biases and lead us to propose a completely different linearized registration model– the All Augmented Model (AAM). AAM employs all OBs and ABs in the state vector and a Kalman filter (KF) to produce their estimates. Numerical simulation results show that the estimated performance of AAM is close to the Cramér-Rao lower bound (CRLB) and that the Root Mean Square Errors (RMSEs) of the rectified measurements by using AAM are more than 500 m smaller than by URM in all directions.

For mobile 3-D radar installed on a gyro-stabilized platform, its measurements are usually contaminated by the systematic biases which contain radar offset biases (i.e., range, azimuth and elevation biases) and attitude biases (i.e., yaw, pitch and roll biases) of the platform because of the errors in the Inertial Measurement Units (IMU). Systematic biases can NOT be removed by a single radar itself; however, fortunately, they can be estimated by using two different radar measurements of the same target. The process of estimating systematic biases and then compensating radar measurements is called error registration. In this paper, the registration models are established first, then, the equivalent radar measurement error expressions caused by the attitude biases are derived and the dependencies among attitude biases and offset biases are analysed by using the observable matrix criterion. Based on the analyses above, an Optimized Bias Estimation Model (OBEM) is proposed for registration. OBEM uses the subtraction of azimuth and yaw bias as one variable and omits roll and pitch biases in the state vector, which decreases the dimension of the state vector from fourteen of the All Augmented Model (AAM), (which uses all the systematic biases of both radars as state vector) to eight and has about 80% reduction in calculation costs. Also, OBEM can decrease the coupling influences of roll and pitch biases and improve the estimation performance of radar elevation bias. Monte Carlo experiments were made. Numerical results showed that the bias estimation accuracies and the rectified radar raw measurement accuracies can be improved.

Besides offset biases (such as range, the gain of range, azimuth, and elevation biases), for mobile radars, platform attitude biases (such as yaw, pitch, and roll biases) induced by the accumulated errors of the Inertial Measurement Units (IMU) of the Inertial Navigation System (INS) can also influence radar measurements. Both kinds of biases are coupled. Based on the analyses of the coupling influences and the observability of 3-D radars’ error registration model, in the article, an Attitude Bias Conversion Model (ABCM) based on Square Root Unscented Kalman Filter (SRUKF) is proposed. ABCM can estimate 3-D radars’ absolute offset biases under the influences of platform attitude biases. It converts platform attitude biases into radar measurement errors, by which the target East-North-Up (ENU) coordinates can be obtained from radar measurements directly without using the rotation transformation, which was usually used in the transition from platform frame to ENU considering attitude biases. In addition, SRUKF can avoid the inaccurate estimations caused by linearization, and it can weaken the adverse influences of the poor attitude bias estimation results in the application of ABCM. Theoretical derivations and simulation results show that 1) ABCM-SRUKF can improve elevation bias estimate accuracy to about 0·8 degree in the mean square error sense; 2) linearization is not the main reason for poor estimation of attitude biases; and 3) unobservability is the main reason.

Background and purpose: Due to the increased complexity of the radiotherapy process in recent years, many QA procedures have been introduced to improve the quality of the treatment preparation and the treatment delivery. Most of these procedures give no direct information on the efficiency of the overall process in the radiotherapy department. It is quite evident that the frequency of errors will affect the efficiency of the process (for instance manpower to solve the detected problems), but also affect more generally the patients' experience.

Material and methods: To make some objective assessment of the erroneous entries in the process of radiotherapy, a pilot study was initiated to report all problems during a period of 2 months on a dedicated QA form. By registration of these errors, the QA group could trace the main sources of the decrease in efficiency and increased waiting time for patients.

Results and conclusions: For 46% of the patients during the two months at least one QA forms was filled in. With these QA forms 198 erroneous entries including 33 erroneous treatments were reported. Regarding the origin of the problems, the majority (60%) were generated in the planning process, while 56% of the errors were detected at the treatment unit before or during delivery of the first fraction. In total 461 actions were undertaken to recover all the reported errors. This generated time delay for 145 patients. Extrapolating these results to a year, a 0.7 full time equivalent person is spent to recover these errors, while the total patient population waits for about 140 hours. Corrective actions have been undertaken to optimise the overall process.