Hardware implementation of a Particle Filter for location estimation

Abstract

In this paper we present the hardware implementation of a Particle Filter for location estimation. Based on distance information to static network nodes, the filter estimates the three-dimensional position of a mobile network node. The design has been derived from a set of formal operation properties and synthesized for an FPGA prototype platform. Accessed through a serial interface, it can be used as a location estimation core from microcontrollers with low computational power. The implemented models for state transition and measurements can be re-parameterized during operation. Due to the chosen design approach these models can also easily be modified or exchanged in order to match the application needs. The correct functionality of the implementation has been shown using real time-of-flight based distance measurements. Therefore, the prototype platform has been integrated in an existing IEEE 802.15.4a compliant wireless network infrastructure.

Elephant Herding Optimization for Energy-Based Localization

“…Therefore, the EHO algorithm implies iteratively applying (5), (6), (7) and (8) for a predefined number of iterations. The parameters like maximum number of iterations and population size are indirectly controlled by the number of clans and clan size, whereas α and β are considered fixed for a certain application.…”

“…[1][2][3][4]. In general, source localization algorithms can be categorized as range-free and range-based [5][6][7][8]. The former ones consider only information about connectivity and usually require a training phase in which a database is constructed [9,10].…”

SmartLoCore: A Concept for an Adaptive Power-Aware Localization Processor

“…Digital Signal Processors (DSP) or Field Programmable Gate Array (FPGA) offer best flexibility or performance, however introduce increased power consumption or cost. They allow for straight-forward application development but on the other hand introduce overheads like increased power consumption or cost [5]. The SmartLoCore approach considers a power optimized hardware architecture, which is integrated in a flexible design approach.…”

“…The transformation is obtained by the integration of the gyroscope sensor readings, i.e., the attitude of the system. To model the system, the state vector δ x comprises at least the errors of acceleration and gyro biases, the attitude, position and velocity, thus having 15 elements (5).…”

Elephant Herding Optimization for Energy-Based Localization

“…[1,2,3,4]. In general, source localization algorithms can be categorized as range-free and range-based [5,6,7,8]. The former ones consider only information about connectivity and usually require a training phase in which a database is constructed [9,10].…”