A Particle Swarm Optimization Based Multilateration Algorithm for UWB Sensor Network Weihua Gao Ganapathi Kamath Kalyan Veeramachaneni Lisa Osadciw Department of Electrical Engineering and Computer Science Syracuse University Syracuse, NY-13244

Overview UWB first responder network Application to Multilateration Particle Swarm Optimization Results

UWB Technology Introduction Frequency Modulation 2.4 GHz Narrowband Communication 1 Time-domain behavior Frequency-domain behavior Impulse Modulation 3 10 GHz frequency Ultrawideband Communication time 1 (FCC Min=1500Mhz) UWB Technology Definition Communication that occupies more than 1.5GHz of spectrum Communication with fractional bandwidth of more than 0.25 Extremely short pulses, no frequency carrier UWB Technology has the ability to: Penetrate Surfaces Position (Indoor and Outdoor) Communication on very high data rate: >500Mbps

UWB Locationing System GPS does not work at indoor or underground environment UWB technology is a promising solution for precision ranging due to its fine time resolution to resolve multipath Defense

Procedure and Challenges LOS ranges and NLOS ranges (Acquiring Ranging Data) Locationing Algorithms (Ranging Data Fusion ) Challenges Not enough LOS ranges, have to NLOS ranges NLOS ranges not usable when there is no info about NLOS ranging errors Indoor Channel Impulse Response

Two frequently used methods are Multilateration Multilateration is a localization technique which uses measurements of the distance between the target and three or more known base locations. Two frequently used methods are Time of Arrival (TOA) Time Difference of Arrival (TDOA) In this presentation we use the ToA

Ideally .. When there are no errors in the distance measurements the intersection of the circles pinpoints the location of the target exactly. Solved using one step multilateration

One step multilateration The problem of Intersecting circles is converted to one of intersecting lines. This problem can be solved in one step.

Conventional Techniques Gradient descent based approaches Taylor series based multilateration Approximations are made on the objective function, to arrive at a Taylor series expansion. A current guess of the target position is iteratively relocated at a new position in the direction in which the error decreases.

Particle Swarm Optimization Evolutionary Algorithm using Swarm Intelligence Modeled on social intelligence of bees, for finding nectar in a field Bees communicate (somehow) the location of source of food to each other Direct search optimization method The Original PSO algorithm was introduced by Kennedy and Eberheart in 1995 A bee at work Photo by Andreas., under a Creative Commons license http://flickr.com/photos/124330160/23764566/in/set-72157600038846587/

How the PSO works Independence of movement, Local influence Each particle is going to keep to its own straight path in the search space. Local influence Each particle is attracted to the best solution that it has seen. Global influence which is communicated Each particle is attracted to the best solution that any one of the particles of the swarm seen Position Update Velocity Update

How the PSO works … Iterate for finite iterations (or convergence) Evaluate the objective function for each particle Update xpbest, xgbest Position Update Velocity Update

PSO based Multilateration Objective function :

Advantages of PSO Uses simple + and * operations Matrix [] manipulations, Parallelizable The objective function is the only place where the square root operation is required. Immune to problems involving singular matrices which arise when inverses are needed in other approaches Overcome local optima due to the swarm behavior. Applicable to dynamically changing and continually evolving fitness landscapes.

Results For a specific case, The steepest descent can have convergence issues.

Results cntd … Avg Localization Error (for 10000 monte-carlo runs) With comparatively simpler processing, the PSO is able to achieve the same average locationing error as the gradient descent.

Conclusions and future work We analyzed the data from a UWB based locationing system, designed an efficient parallel algorithm for fusing three or more measurements for accurate locationing. More analysis of the algorithm is under way.

References W. Gao, K Veeramachaneni, G. Kamath and L. Osadciw, “A novel ultra wide band locationing system using swarm enabled learning approaches”, IEEE swarm intelligence symposium, Nashville, TN, USA. March 2009 “Localization in Sensor Networks”, Bachrach and Taylor

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