Analysis of Mobile 3-D Radar Error Registration when Radar Sways with Platform

Abstract

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. K E Y

Quaternionic analysis of the influences of systematic biases on radar data processing

“…As discussed in [4], two different situations ((a) Radar installed on the gyro-stabilized platform, and (b) Radar installed on the platform directly) are discussed respectively.…”

“…(a) Radar installed on the gyro-stabilized platform [4] In this situation, as shown in Figure 1 Similarly to (6a) and (7a), the quaternion representation for attitude biases can be written as:…”

A New Spatial Registration Algorithm of Aerial Moving Platform to Sea Target Tracking

“…Ref. [25] divides the estimation of sensor observation systematic error and attitude angle error into two steps. Firstly, the attitude angle is set to zero and the Kalman filter is used to estimate the sensor observation systematic error; then the sensor observation is corrected by the obtained sensor systematic error and then the attitude angle error is estimated by the Kalman filter, but this method does not consider the coupling effect of the attitude angle error on the sensor error.…”