Cooperative Geolocation Research Articles

Robust Localization Using Range Measurements With Unknown and Bounded Errors

Cooperative geolocation has attracted significant research interests in recent years. A large number of localization algorithms rely on the availability of statistical knowledge of measurement errors, which is often difficult to obtain in practice. Compared with the statistical knowledge of measurement errors, it can often be easier to obtain the measurement error bound. This paper investigates a localization problem assuming unknown measurement error distribution except for a bound on the error. We first formulate this localization problem as an optimization problem to minimize the worst case estimation error, which is shown to be a nonconvex optimization problem. Then, relaxation is applied to transform it into a convex one. Furthermore, we propose a distributed algorithm to solve the problem, which will converge in a few iterations. Simulation results show that the proposed algorithms are more robust to large measurement errors than existing algorithms in the literature. Geometrical analysis providing additional insights is also provided.

Modified CRLB for Cooperative Geolocation of Two Devices Using Signals of Opportunity

We consider the problem of localizing two devices using signals of opportunity from beacons with known positions. Beacons and devices have asynchronous local clocks or oscillators with unknown clock skews and offsets. We model clock skews as random, and analyze the biases introduced by clock asynchronism in the received signals. By deriving the equivalent Fisher information matrix for the modified Bayesian Cramér-Rao lower bound (CRLB) of device position and velocity estimation, we quantify the errors caused by clock asynchronism. We propose an algorithm based on differential time-difference-of-arrival (DTDOA) and frequency-difference-of-arrival (FDOA) that mitigates the effects of clock asynchronism to estimate the device positions and velocities. Simulation results suggest that our proposed algorithm is robust and approaches the CRLB when clock skews have small standard deviations.

Cooperative Estimation Using Mobile Sensor Nodes in the Presence of Communication Loss

Cooperative estimation using multiple mobile sensor nodes communicating over a lossy network is considered. A new method, termed predicted information, is developed from a separable formulation of the extended information filter. Two variations of the predicted information method are presented, which trade between accuracy and computational complexity. The first variation estimates the information matrix updates directly from estimates of the cooperating sensor node states. The second variation uses a piecewise constant approximation to predict the informationmatrixupdates.Thepredictedinformation methodisshowntogivetheexactsolutionforlinearsystems when the measurement dynamics are constant or known by all sensor nodes. The predicted information method is evaluated with a cooperative geolocation problem with two uninhabited aerial vehicles using gimballing camera sensors. Flight-test data and high-fidelity hardware-in-the-loop simulations are used to compare the predicted information method with three benchmark methods from the literature for tracking both stationary and maneuvering targets and for single extended losses and random dropouts.

Decentralized Geolocation and Bias Estimation for Uninhabited Aerial Vehicles with Articulating Cameras

The cooperative geolocation of a point of interest using multiple uninhabited aerial vehicles with articulating camera sensors is addressed, where there are non-zero-mean errors (biases) in the estimate of the uninhabited aerial vehicle state. The proposed approach is to use the onboard navigation solution in the estimator and, further, to consider biases across all uninhabited aerial vehicles and to jointly estimate both the biases and the unknown point-of-interest location. Furthermore, a decentralized solution is presented that uses marginalization of the biases, thus allowing the uninhabited aerial vehicles to share only information about the point of interest and model only their local biases. This decentralized approach saves significant computation and scales well with the number of uninhabited aerial vehicles. Real flight-test data and hardware-in-the-loop simulations are used to demonstrate the improvement in geolocation with bias estimation, as well as the effectiveness of the new decentralized point of interest and bias estimation algorithm, for both stationary and moving points of interest.

Vision-Based Target Geolocation UsingMicro Air Vehicles

A method for determining the location of a stationary ground target when imaged from the air using a camera-equipped micro air vehicle is developed. The ground object's elevation is assumed known. Rather than a one-shot affair, multiple bearing measurements of the ground object taken as the aircraft flies around the target are used. This makes it possible for the adverse effects of both the random measurement errors and the systematic measurement errors (i.e., attitude sensors' biases) to be mitigated. The main result of this paper is that when bearing measurements of a target are taken over time, the target's position and the micro air vehicle's systematic attitude-measurement errors can be jointly estimated using linear regression. As a result,the target is accurately geolocated and the attitude sensors are calibrated. If only one snap bearing measurement of the target can be taken, the attitude sensors will be calibrated before arrival at the target area using an initial point The technique was successfully tested on actual flight data collected during multiple micro air vehicle operations for cooperative geolocation of unknown targets.