Positioning of Radio Emission Sources with Unmanned Aerial Vehicles using TDOA-AOA Measurement Processing

Actual trends in current passive geolocation system development includes cooperation of flying segment based on receiver stations aboard Unmanned Aerial Vehicles (UAVs) with terrestrial segment including stationary ground receiver stations. Existing accuracy results achieves the order of tens and hundreds of meters in optimistic Line of Sight (LOS) conditions. However, the problem of radio emission sources positioning with UAVs is especially relevant for search and rescue operations in heterogeneous terrain, when separate primary measurements obtained, for example, after reflections, could lead to a significant error. One possible way to improve the accuracy of positioning in such conditions is to use aerial passive geolocation based on UAVs with joint processing of Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) primary measurements. The contribution of the current investigation is the development of mathematical model for positioning of radio emission sources with UAVs using TDOA-AOA measurement processing.This work was supported by the Ministry of Science and Education of the Russian Federation with Grant of the President of the Russian Federation for the state support of young Russian scientists № MK-3468.2018.9

Mobile emitter geolocation and tracking using TDOA and FDOA measurements

This paper considers recursive tracking of one mobile emitter using a sequence of time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement pairs obtained by one pair of sensors. We consider only a single emitter without data association issues (no missed detections or false measurements). Each TDOA measurement defines a region of possible emitter locations around a unique hyperbola. This likelihood function is approximated by a Gaussian mixture, which leads to a dynamic bank of Kalman filters tracking algorithm. The FDOA measurements update relative probabilities and estimates of individual Kalman filters. This approach results in a better track state probability density function approximation by a Gaussian mixture, and tracking results near the Cramér-Rao lower bound. Proposed algorithm is also applicable in other cases of nonlinear information fusion. The performance of proposed Gaussian mixture approach is evaluated using a simulation study, and compared with a bank of EKF filters and the Cramér-Rao lower bound

Non-Linear Optimization Applied to Angle-of-Arrival Satellite-Based Geolocation

Geolocation is a common application for satellite systems. This involves estimating an object\u27s location (herein called the subject) based on noisy satellite data. Many geolocation methods exist; however, none are tailored specifically for the unique problems faced by satellite systems. Some satellites are so far from the subject being localized that by the time the satellite receives a signal from the subject it might have moved appreciably. Furthermore, some satellites or terrestrial sensors may be much closer to the subject than others. Therefore, sensors may need to be weighted based upon their distance to the subject being localized. In addition, even if a subject can be localized, the confidence in this localization may be unknown. Non-linear optimization is proposed, implemented, and analyzed as a means of geolocating objects and providing confidence estimates from passive satellite line-of-sight data. Non-linear optimization requires an initial estimate. This estimate is provided by a triangulation method. The non-linear optimization then improves upon this estimate iteratively by finding estimates that are more likely to have produced the observed line-of-sight measurements. The covariance matrix of the geolocation parameters being estimated is naturally produced by the optimization which provides quantified confidence in the geolocation estimate. Simulations are developed to provide a means of evaluating the performance of the non-linear optimization algorithm. It was found that non-linear optimization can reduce the average error in geolocation estimates, provide improved estimation confidence, and accurately estimate its geolocation confidence for some subjects. The results from the theoretical development of the non-linear optimization algorithm and its simulated performance is quantified and discussed

An Analysis of Radio-Frequency Geolocation Techniques for Satellite Systems Design

This research 1) evaluates the effectiveness of CubeSat radio-frequency geolocation and 2) analyzes the sensitivity of different RF algorithms to system parameters. A MATLAB simulation is developed to assess geolocation accuracy for variable system designs and techniques (AOA, TDOA, T/FDOA). An unconstrained maximum likelihood estimator (MLE) and three different digital elevation models (DEM) are utilized as the surface of the Earth constraint to improve geolocation accuracy. The results presented show the effectiveness of the MLE and DEM techniques, the sensitivity of AOA, TDOA, and T/FDOA algorithms, and the system level performance of a CubeSat geolocation cluster in a 500km circular orbit

Exploiting Structural Signal Information in Passive Emitter Localization

The operational use of systems for passive geolocation of radio frequency emitters poses various challenges to single sensor systems or sensor networks depending on the measurement methods. Position estimation by means of direction finding systems often requires complex receiver and antenna technique. Time (Difference) of Arrival methods (TDOA, TOA) are based on measurements regarding the signal propagation duration and generally require broadband communication links to transmit raw signal data between spatially separated receivers of a sensor network. Such bandwidth requirements are particularly challenging for applications with moving sensor nodes. This issue is addressed in this thesis and techniques that use signal structure information of the considered signals are presented which allow a drastic reduction of the communication requirements. The advantages of using knowledge of the signal structure for TDOA based emitter localization are shown using two exemplary applications. The first case example deals with the passive surveillance of the civil airspace (Air Traffic Management, ATM) using a stationary sensor network. State of the art airspace surveillance is mainly based on active radar systems (Primary Surveillance Radar, PSR), cooperative secondary radar systems (Secondary Surveillance Radar, SSR) and automatic position reports from the aircraft itself (Automatic Dependent Surveillance-Broadcast, ADS-B). SSR as well as ADS-B relies on aircrafts sending transponder signals at a center frequency of 1090 MHz. The reliability and accuracy of the position reports sent by aircrafts using ADS-B are limited and not sufficient to ensure safe airspace separation for example of two aircrafts landing on parallel runways. In the worst case, the data may even be altered with malicious intent. Using passive emitter localization and tracking based on multilateration (TDOA/hyperbolic localization), a precise situational awareness can be given which is independent of the content of the emitted transponder signals. The high concentration of sending targets and the high number of signals require special signal processing and information fusion techniques to overcome the huge amount of data. It will be shown that a multilateration network that employs those techniques can be used to improve airspace security at reasonable costs. For the second case, a concept is introduced which allows TDOA based emitter localization with only one moving observer platform. Conventional TDOA measurements are obtained using spatially distributed sensor nodes which capture an emitted signal at the same time. From those signals, the time difference of arrival is estimated. Under certain conditions, the exploitation of signal structure information allows to transfer the otherwise only spatial into a spatial and temporal measurement problem. This way, it is possible to obtain TDOA estimates over multiple measurement time steps using a single moving observer and to thus localize the emitter of the signals. The concept of direct position determination is applied to the single sensor signal structure TDOA scheme and techniques for direct single sensor TDOA are introduced. The validity and performance of the presented methods is shown in theoretical analysis in terms of Cramér-Rao Lower Bounds, Monte-Carlo simulations and by evaluation of real data gained during field experiments