Optimal MLAT Tracking Based on Improved Simulation for Airport Surveillance

2013 ◽  
Vol 341-342 ◽  
pp. 631-634
Author(s):  
Yu Lu ◽  
Dong Lin He ◽  
Hui Yao
Keyword(s):  
Measurement Error ◽  
Simulation Method ◽  
Time Difference ◽  
Time Of Arrival ◽  
Practical Application ◽  
Dilution Of Precision ◽  
Optimal Tracking ◽  
Clock Error ◽  
Geometric Relation ◽  
Geometric Dilution Of Precision

MLAT (Multilateration) is becoming one of the leading techniques in the airport surveillance. It locates the positions of aircrafts by the TDOAs (Time Difference of Arrivals) of signal from the aircraft to the different remote stations. In views of the influence from the geometric relation and the clock error among different stations, an improved simulation is proposed to estimate the measurement error for the optimal tracking. The model of simulation takes both the GDOP (Geometric Dilution of Precision) and TOA (Time of Arrival) error into consideration. Experiment results show that compared with the traditional simulation method, the improved simulation is more accurate and is able to be used in the practical application.

scholarly journals TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 782
Author(s):  
Shuo Cao ◽  
Honglei Qin ◽  
Li Cong ◽  
Yingtao Huang
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Satellite System ◽  
Time Of Arrival ◽  
Military Operations ◽  
Position Information ◽  
Cooperative Navigation ◽  
Navigation Algorithm ◽  
Geometric Dilution Of Precision ◽  
Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

Signal Measurement Error Analysis of the Bayesian Network of Mechanical Fault Detection

2014 ◽  
Vol 945-949 ◽  
pp. 2183-2186
Author(s):  
Yun Xia Zhang ◽  
Ling Lan ◽  
Xiao Hui Wang
Keyword(s):  
Measurement Error ◽  
Fault Detection ◽  
Error Analysis ◽  
Bayesian Network ◽  
Mechanical System ◽  
Gibbs Sampling ◽  
Sampling Method ◽  
Practical Application ◽  
Denoising Method ◽  
Signal Measurement

Based on measurement error of observation nodes is commom in mechanical system fault detection, but the traditional denoising method has many shortcomings. This paper introduce the Gibbs sampling method, which can be used to denoise and eliminate measurement error for node discreted information. We discuss it, and expect some promotion in practical application.

Research on Maneuvering Trajectories Optimization in Bearings-Only Localization for Single Observer

2012 ◽  
Vol 433-440 ◽  
pp. 5946-5950 ◽  
Author(s):  
Zhang Song Shi ◽  
Rui Li ◽  
Hang Yu Wang
Keyword(s):  
Genetic Algorithm ◽  
Lower Bound ◽  
Performance Index ◽  
Constant Velocity ◽  
Dilution Of Precision ◽  
Localization Precision ◽  
Course Sequence ◽  
Geometric Dilution Of Precision

Based on Crammer-Rao Lower Bound (CRLB), this paper adopts Geometric Dilution of Precision (GDOP) as the optimizing performance index to analyze the localization precision in Bearing-only localization for single observer. And the genetic algorithm is employed to calculate the optimal course sequence. The movement straight with constant velocity and movement with constant prefix angles are analyzed. Simulations show that maneuvering trajectory is propitious to improve localization precision.

KNOWLEDGE-BASED METHODS FOR OPTIMUM APPROXIMATION OF GEOMETRIC DILUTION OF PRECISION

2010 ◽  
Vol 09 (02) ◽  
pp. 153-170 ◽  
Author(s):  
M. R. MOSAVI
Keyword(s):  
Measurement Errors ◽  
Adaptive Filters ◽  
Essential Feature ◽  
Positioning System ◽  
Dilution Of Precision ◽  
Knowledge Based ◽  
Gps Satellites ◽  
Satellite Geometry ◽  
Influence Measurement ◽  
Geometric Dilution Of Precision

Global Positioning System (GPS) satellites signal processing to obtain all in view satellite measurements and to use them to find a solution and to do integrity monitoring forms a major component of the load on the receiver's processing element. If processing capability is limited there is restriction on the number of measurements which can be obtained and processed. Alternatively, the number of measurements can be restricted and the resulting saving in load on the processor can be used to offer more spare processing time which can be used for other user specific requirements. Thus if m visible satellites can provide measurements only n measurements can be used (n < m). The arrangement and the number of GPS satellites influence measurement accuracy. Dilution of Precision (DOP) is an index evaluating the arrangement of satellites. Geometric DOP (GDOP) is, in effect, the amplification factor of pseudo-range measurement errors into user errors due to the effect of satellite geometry. The GDOP approximation is an essential feature in determining the performance of a positioning system. In this paper, knowledge-based methods such as neural networks and evolutionary adaptive filters are presented for optimum approximation of GDOP. Without matrix inversion required, the knowledge-based approaches are capable of evaluating all subsets of satellites and hence reduce the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. Models validity is verified with test data. The results are highly effective techniques for GDOP approximation.

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