On Distinguishing the Multiple Radio Paths in RSS-based Ranging

Dian Zhang, Yunhuai Liu, Xiaonan Guo, Min Gao and Lionel M. NiCollege of Software, Shenzhen University

Department of Computer Science and Engineering, Hong Kong University of Science and Technology,

Third Research Institute of Ministry of Public Security

20121029 TY

Outline

• Introduction• Observation• Problem definition• Treatments of problem• Experiment • Evaluation • Conclusion

Introduction

• RSS-based ranging is apt to be affected by the multipath phenomenon

• Previous works try to profile the environment and refer this profile in run-time– Site survey

• In practical dynamic environments, however, the profile frequently changes and the painful retraining is needed

Introduction

• Key observation– Given a pair of nodes, the RSS at different

spectrum channels will be different

• By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight path

Introduction

• Non-Linear Curvature Fitting Problem– An ill-conditioned problem– Practical considerations to improve the

conditioning shape

• MuD (Multipath Distinguishing) System– Real-Time indoor tracking system – Very different from us

Observation

• Multipath– Atmospheric duct– Reflection– Refraction

• Friss Model cannot work• Profile-based approaches– Labor-Intensive– Cannot adapt to dynamic environment

Observation

• Frequency diversity may help – significantly different RSS values at different

spectrum channels – quite stable at the same environment

• By carefully analyzing these RSS, we can identify the amplitudes and phases of signals from each path, then derive the accurate distance according to the amplitude of LOS signals

Observation

Observation

• Radio propagation in free space (LOS path)– Friss Model– Pt is the transmission power

– Gt , Gr are the antenna gain of the transmitter and receiver

– λ is the signal wavelength – d is the LOS path length– Path strength– Path phase

Multipath & Frequency Diversity

• Reflection and Refraction (NLOS path)– Reflection/Refraction Coefficient

– path strength

– Path phase is the same– d is not the LOS distance anymore

Multipath & Frequency Diversity

• Path phase– Change when the signal frequency changes

• phase-shift– Change when the signal path changes– Path amplitude will be affected – TelosB• Frequency : 2.4G ~ 2.4835G• Number of Channels : 16• Wave length : 0.125m ~ 0.1208m

Multipath & Frequency Diversity

Multipath & Frequency Diversity

a = 2m λ1= 0.125 mb = 3m λ2= 0.1208 m

• Path Phase changes when the signal frequency changes – Path a

• Path shift when the signal path changes– Compare Path a with Path b

Multipath & Frequency Diversity

• Assumptions– transmitters and receivers are free to dynamically

adjust their frequency in run time– transmitter and receiver are well coordinated and

synchronized• Environment variable – Number of propagation path : n– Number of channel : m

Problem definition

• System model i [1, n], j [1, ∈ ∈m]– Path length : di (d1 is the LOS path)– Reflection coefficient : Γi (Γ1 = 1)– Wave length : λj

– For a fixed pair of tx & rx :

Problem definition

• For a given , we have the amplitude of the vector sum is a function :

Problem definition

• In run time, we will have a RSS measurement for each channels

• non-linear curvature fitting problem

• What we want is : d1

Problem definition

• Use Numerical Iteration to approximate this problem

– J is the Jacobian matrix– H is the Hessian matrix

• This is an ill-conditioned matrix– Even when n = 1 and m = 2, the condition number

of the Hessian matrix is

Problem definition

• Ill-conditioned Matrix– Condition number is very large– matrix of condition number = 100 will have 100%

changes on the solution when the input changes 1%

Problem definition

• Ill-conditioned Matrix– In practical calculation:

Problem definition

Treatments of problem

• Derivation of fixed Unknows

– Hardware Specification Manuals– Chamber Training– Online Training

Treatments of problem

• Reducing the Model Path Number (n)– NLOS paths with three or more reflections or

refractions can be ignored• For typical materials, Γ is around 0.5 or less• Three or more reflections on a single NLOS path will

consume more than 87.5% of the energy– Ignore the paths which are extremely long, say

more than two times of the LOS path• energy will fade inversely proportional to the square of

the path length• 2 times => 0.25 × 0.5 = 0.125

Treatments of problem

• Bounding the Unknowns initial value d1

– Newton’s method is quite effective when the initial setting is close to the solution enough

– Consider the limited mobility in the indoor environments

– Upper bound and lower bound • old real d1 - 1 ≤ Initial d1 ≤ old real d1

• old real d1 ≤ Initial di ≤ 2 * old real d1

Experiment

• MuD System – The tracking target wears a transmitter– 3 anchor nodes as receivers connected to server – Server solve the minimize problem, apply the

trilateration algorithm, and display the location– 20 x 20 m2

– Transmission power : -10 dbm– 16 channels– Transmitter stay in one channel for 50 ms

Experiment

Experiment

Evaluation

• The Impact of Assumed Multipath Number– n = 5 outperforms the other settings with over

65% of ranging having the error less than 20%

Evaluation

• The Impact of c– chamber approach performs the best accuracy– online training approach exhibits a similar result

Evaluation

• The Impact of Used Channel Numbers– we suggest more channels when they are

available and when latency and measurement overhead are not the concern

Evaluation

• The Impact of Initial Value Setting in MuD– Only when the initial value is set in a reasonable range, a reasonable solutions is expected

Evaluation

• Accuracy – The improvement with distinguishing multipath is

up to 10 times

Evaluation

• Latency of MuD Tracking System– 16 channels– Transmitter stay in one channel for 50 ms – The channel switching time is about 0.34 ms

Evaluation

• Comparison with Static Environment– They both have averaged error about 1 meter– The difference between them is less than 5%

Conclusion

• We propose to exploit the frequency diversity of the radio propagation paths to mine the phase information of the radio paths and identify the signal amplitude of the LOS path

• Implement a real-time tracking system • Experimental results show that the ranging

and localization errors are 1m in average in a 20m×20m laboratory

Conclusion

• Future work– study the scenarios when LOS path does not exist

• Q&A