EE Seminar: Performance Enhancement of Positioning Systems Using Sources of Opportunity
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Speaker: Eilon Regev,
M.Sc. student under the supervision of Prof. Anthony Weiss
Wednesday, April 6, 2016 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering
Performance Enhancement of Positioning Systems Using Sources of Opportunity
Abstract
The importance of the field of positioning has substantially grown in recent years. Alongside military applications, which were the focus for many years, civil and medical applications are in development, as identifying people in smoke-filled structures, search and rescue, the location of objects' logistical needs, intruder identification and the evolving civil application of indoor navigation.
One of the important parameters of positioning systems, regardless of the operating technique, is evaluating system performance. The most important parameter in such an examination is the assessment of positioning accuracy. This paper studies improvement to system performance which can be achieved by adding cooperative Emitters with known locations. Cooperative Emitters of this kind allow improvement of the system's immunity to systematic errors. Systematic error refers to each component correlative between multiple measurements.
This study presents a number of different models of systematic errors. The theoretical analysis of each model is independent from the measurement technique but for practical examination and testing of model simulations, the TOA method was used:
A. A) Multiple Sensors – each sensor performs a number of measurements towards the target. All measurement error consists of two components: random error and systematic error. Adding Cooperative Emitters creates a correlation between the noise component from the target measurement and the noise component from the measurement to the cooperative emitters.
B. B) Single sensor measurements operating in motion – the sensor has a systematic error (random for a single set of measurements). We assume that the systematic error in this case varies slowly in relation to the total time in which the measurement is used for approximating the target location. In this case, we may assume that the error is fixed for all measurements. Adding cooperative emitters, with measurements obtained from the same systematic error, will allow better revaluation of the systematic error component.
C. C) Positioning error of the sensor during measurements – As this model deals with a moving sensor, the systematic error factor stems from the registration sensor positioning inaccuracy at the time of measurement.
D. D) The speed of the signal in medium - this model is particularly suited for sound-wave-based measurements. The speed of the signal depends on the wind speed and direction.
E. E) Multiple targets positioning – this model is equivalent to a model in which the cooperative emitters location is unknown. It examines the effect of pinpointing a number of targets for each measurement when a random error and a systematic error, common to all measurements, occur with the same sensor.
A theoretical development based on Maximum Likelihood estimation is presented for each error model. This is the optimal estimator which achieves Cramer Rao Lower Bound. The aim of this development is to calculate CRLB as a function of system parameters: sensors positioning with respect to the target (system geometry), the number of measurements from each sensor, noise power ratio between random and systematic noise power, and error distribution. This study presents the benefits of using cooperative emitters in accordance to each of these parameters.
The system geometry, affected by the sensors location, cooperative emitter’s location and the target location, is the most difficult parameter to model (due to the multiplicity of degrees of freedom). Nevertheless, in order to get an indication of the connection between the achieved improvement and the system geometry, an approximation was used for TOA method. Such an approximation allows describing the geometry of the system (approximately) as one parameter: the angle in which the sensors are scattered around the target.
In order to verify the correctness of theoretical development for each individual model - positioning simulation is performed, based on TOA. Measurement samples are generated according to the particular error model, and based on those measurements the positioning is executed. This positioning is based on the linear approximation of the distribution density function around the estimated target location. The obtained positioning results and errors are compared with the expected value of theoretical development.
The results of this work provide a tool for testing the feasibility of the addition of cooperative emitters to the system. Viewing the achieved improvement as dependent in each one of the parameters allows designers to test the feasibility of additional cooperative emitters in contrast to other alternatives: increasing the number of measurements, the use of more precise sensors and change to the layout of the sensors – sensor position adjustment during measurements.
