Sanvidha C. K. Herath

Optimal Sensor Arrangements in Angle of Arrival (AoA) and Range Based Localization with Linear Sensor Arrays

This paper investigates the linear separation requirements for Angle-of-Arrival (AoA) and range sensors, in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyse the sensor-target geometry in terms of the Cramer–Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear spatial distribution of sensors. Here in this paper, we consider both fixed and adjustable linear sensor arrays.

Optimal sensor separation for AoA based localization via linear sensor array

This paper investigates the linear separation requirements for Angle-of-Arrival (AoA) sensors, in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyse the sensor-target geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear spacial distribution. Here we consider a situation where one sensor is fixed and the rest are free to be positioned in a linear array.

Multitarget Tracking via Space Transformations Using a Single Frequency Continuous Wave Radar

This paper investigates the location and velocity estimation problem involving multiple targets using the phase difference and frequency shift of the returned Doppler modulated signal. The minimal receiver configuration that addresses the data association and missing information problem is presented for the case of linear arrays. Non-linearly modeled Doppler radar measurements are used to obtain an accurate estimate of the target dynamics progressively in a linear framework utilizing a recently developed robust state estimation approach.

Robust Localization With Minimum Number of TDoA Measurements

This letter looks at the theoretical conditions underpinning unique localization of an emitter using Time-Delay-of-Arrival(TDoA) from minimum number of sensors in 2-D and 3-D space. A discussion is carried out on the unique localization region with the TDoA measurements subjected to a bounded error. For both 2-D and 3-D, error bounds have been found, beyond which, there is no existence of the unique solution region.

Tracking multiple mobile agents with single frequency Continuous Wave radar

In this paper, a novel concept to determine the velocity and the location information of multiple mobile agents using Doppler radar has been introduced. Also, an expression for the minimum number of inline sensors needed to guarantee this estimation for n number of mobile agents has been obtained. Current methods use the time derivative of the displacement of adjacent position measurements to find the velocities of agents. This method is error prone, particularly, if the agents are accelerating. In our approach we incorporate direction-of-arrival (DOA) radar which tracks the location and the velocity of each and every agent in each measurement step.

Optimal sensor placement in range based localization for linear arrays

This paper investigates the linear separation requirements for range sensors in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyze the sensortarget geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear spacial distribution.

Localization with ghost elimination of emitters via time-of-arrival measurements

This paper looks at the theoretical conditions underpinning unique localization of synchronized multiple emitters using Time-of-Arrival measurements subjected to the data-association problem. The necessary fundamental requirements to solve the so-called ghost node problem associated with sensor arrays are examined. We derive a measurement bound for ideal situations and the underlying concepts are illustrated via simulations.

Optimal sensor placement in linear arrays: Part I — AoA based localization

In this paper, we examine the optimal linear separation requirements for AoA sensors, in order to achieve the best performance in estimating the position of a target subjected to noisy measurements. Cramer-Rao inequality and the corresponding Fisher information matrix are used to analyze the sensor-target geometry, in order to characterize localization performance with respect to the linear spacial distribution of sensors.

Localization with ghost elimination of emitters via time-delay-of-arrival measurements

This paper investigates the theoretical requirements for unique localization of multiple emitters using time delay of arrival (TDoA) subjected to the data-association problem. Specifically, an examination is carried out to find the necessary fundamental requirements to solve the so-called ghost node problem pertaining to sensor arrays.

Mobile Agent Tracking with Single Frequency Continuous Wave Radar: A Linear Robust Filtering Based Approach⋆

Abstract We present a novel filtering approach to determine the position and velocity information of mobile agents which essentially employs a recently developed linear robust estimation idea as the core approach. Prominent problems engulfing implementations of this nature also include data association and missing information and we directly addressed these and provide a generalized solution to the overall problem. A coherent argument for the realistic usage of robust linear filtering over data collected by a linear phase array is aimed at presenting a generic approach for multiple agent tracking. The underlying system uses Doppler radar measurements and accurately estimate the position and velocity of the mobile agent progressively while addressing the issues of data association and missing information.