“…For UGSP mobile radar, the measurement frame is parallel to the body frame (see Figure 1(a)). Its y axis denotes zero-degree azimuth, and clock-wise direction denotes the increment of azimuth.The subscript “ p ” (so-called “platform”) was used to identify body frame, and “ s ” denotes sensor frame. ENU frame has the same origin with the body frame; its x, y , and z axis denotes east, north, and up, respectively. Earth-centred Earth-fixed (ECEF) frame (Progri, 2011; 2014) has its origin located at the centre of the earth (Wang et al, 2013): its x- axis passes through the Greenwich meridian, its z- axis coincides with the Earth's axis of rotation and its y- axis lies in the equatorial plane to form a right-handed coordinate system.Figure 1.Illustration of UGSP mobile radar. (a) Illustration of body frame and sensor frame; (b) Conversion from body frame to ENU frame.…”
Section: Problem Descriptionmentioning
confidence: 99%
“…Multi-sensor data fusion has been widely used in modern civilian and military areas because sensor networks can provide more comprehensive information and more accurate state estimation of the target than a single sensor, such as Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C 4 ISR) and air-control systems (Progri, 2011; 2014). However, before the benefits of multi-sensor integration can be realized, the sensor registration problem (or alignment) must be addressed because of the existence of unavoidable systematic biases (SBs) which make the measurements deviate from the true target coordinates (TTCs).…”
Section: Introductionmentioning
confidence: 99%
“…According to different installation methods, mobile GSP or UGSP radar registration models should be considered because of different types of radar measurements (Wang et al, 2012; Bhatti and Ochieng, 2007). First, GSP radars are installed on a platform that can steadily follow a local ENU frame (Progri, 2011; 2014) by using real-time attitude information provided by the platform INS; i.e., the radar is insulated from the platform AAs. ABs in this situation assume a set of Euler angles between radar sensitive and ENU axes.…”
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
“…For UGSP mobile radar, the measurement frame is parallel to the body frame (see Figure 1(a)). Its y axis denotes zero-degree azimuth, and clock-wise direction denotes the increment of azimuth.The subscript “ p ” (so-called “platform”) was used to identify body frame, and “ s ” denotes sensor frame. ENU frame has the same origin with the body frame; its x, y , and z axis denotes east, north, and up, respectively. Earth-centred Earth-fixed (ECEF) frame (Progri, 2011; 2014) has its origin located at the centre of the earth (Wang et al, 2013): its x- axis passes through the Greenwich meridian, its z- axis coincides with the Earth's axis of rotation and its y- axis lies in the equatorial plane to form a right-handed coordinate system.Figure 1.Illustration of UGSP mobile radar. (a) Illustration of body frame and sensor frame; (b) Conversion from body frame to ENU frame.…”
Section: Problem Descriptionmentioning
confidence: 99%
“…Multi-sensor data fusion has been widely used in modern civilian and military areas because sensor networks can provide more comprehensive information and more accurate state estimation of the target than a single sensor, such as Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C 4 ISR) and air-control systems (Progri, 2011; 2014). However, before the benefits of multi-sensor integration can be realized, the sensor registration problem (or alignment) must be addressed because of the existence of unavoidable systematic biases (SBs) which make the measurements deviate from the true target coordinates (TTCs).…”
Section: Introductionmentioning
confidence: 99%
“…According to different installation methods, mobile GSP or UGSP radar registration models should be considered because of different types of radar measurements (Wang et al, 2012; Bhatti and Ochieng, 2007). First, GSP radars are installed on a platform that can steadily follow a local ENU frame (Progri, 2011; 2014) by using real-time attitude information provided by the platform INS; i.e., the radar is insulated from the platform AAs. ABs in this situation assume a set of Euler angles between radar sensitive and ENU axes.…”
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
“…As distinct from stationary radar, mobile radar measurements contain attitude biases of platform at the same time (Progri et al, 1998; Progri, 2011; Setoodeh et al, 2007; Falcone et al, 1998). Attitude biases, which means yaw, pitch, and roll biases, can be caused by the accumulated biases in the gyros in the Inertial Measurements Units (IMU) of the Inertial Navigation System (INS) (King 1997).…”
Section: Introductionmentioning
confidence: 99%
“…Progri (2011, Chapter 5) provides the most comprehensive description on best blind adaptive algorithms which could be utilized for future applications of mobile radar error registration algorithms of both stationary and non-stationary radars.…”
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.
K E Y WO R D S1. Error Registration.2. Attitude Bias. 3. All Augmented Model (AAM). 4. Optimized Bias Estimation Model (OBEM).
In some Global Positioning System (GPS) signal propagation environments, especially in the ionosphere and urban areas with heavy multipath, GPS signal encounters not only additive noise but also multiplicative noise. In this paper we compare and contrast the conventional GPS signal acquisition method which focuses on handling GPS signal acquisition with additive noise, with the enhanced GPS signal processing under multiplicative noise by proposing an extension of the GPS detection mechanism, to include the GPS detection model that explains detection of the GPS signal under additive and multiplicative noise. For this purpose, a novel GPS signal detection scheme based on high order cyclostationarity is proposed. The principle is introduced, the GPS signal detection structure is described, the ambiguity of initial PseudoRandom Noise (PRN) code phase and Doppler shift of GPS signal is analysed. From the simulation results, the received GPS signal at low power level, which is degraded by additive and multiplicative noise, can be detected under the condition that the received block of GPS data length is at least 1·6 ms and sampling frequency is at least 5 MHz. K E Y WO R D S 1. Weak GPS Signal Acquisition.2. Multiplicative Noise. 3. Doppler shift. 4. Cyclostationarity.
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