Estimation, Localization, and Target Tracking Chitra R. Karanam*, - - PowerPoint PPT Presentation

Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking

Chitra R. Karanam*, Belal Korany*, and Yasamin Mostofi

(*Equal contribution)

Department of Electrical and Computer Engineering University of California Santa Barbara

17th ACM/IEEE International Conference on Information Processing in Sensor Networks, Porto, Portugal

Angle-of-Arrival Estimation

• Important traditional problem

with established solutions

• Several applications
• Beamforming, MUSIC, ESPRIT
• Requires signal phase measurements at

receiver array

• Needs synchronization across antennas
• Difficult to measure on off-the-shelf devices

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Magnitude-Based AoA Estimation

Magnitude/power – easily measurable on off-the-shelf devices

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Signal magnitude plot

Estimate angle-of-arrival with only signal magnitude

Estimate of AoAs

Localization and Tracking

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Source localization Target tracking Crowd counting and spatial behavior Sensor network localization

Outline

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• Magnitude-Based AoA Estimation
• Show it possible to extract AoA information from power

spectral density of receptions

• Extensive experiments: Angular localization of active and

passive sources

• Magnitude-Based Target Tracking
• Duality between AoA estimation and target tracking
• A new framework for target tracking with a very small number
• f transceivers
• Extensive experiments: Active and passive target tracking
• Conclusions

Outline

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• Magnitude-Based AoA Estimation
• Show it possible to extract AoA information from power

spectral density of receptions

• Extensive experiments: Angular localization of active and

passive sources

• Magnitude-Based Target Tracking
• Duality between AoA estimation and target tracking
• A new framework for target tracking with a very small number
• f transceivers
• Extensive experiments: Active and passive target tracking
• Conclusions

Magnitude-Based Framework

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• The complex baseband received

signal as a function of

Path amplitude Phase at 1st antenna

• Power spectral density from spatial autocorrelation function of

Constants Total power

AoA Estimation

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Spectrum of autocorrelation Obtain Peaks in spectrum are located at Problem: Given , estimate Ambiguities: Mirroring, translation, translation + mirroring, other ambiguities Equivalent to placing points on a line, given the pairwise distances Solution: Place a reference source at a known angle

AoA Estimation Framework – Algorithm and Example

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• Add reference source at
• Let the actual be
• Terminate: Solutions are sets with pairwise distance set

1 0.7

1 0.7

0.3

1 0.3

0.4

0.4 0.4

0.6

0.6 0.6 1 1 1 0.4 0.4 0.6 1 0.3 0.6 0.7

Algorithm:

How do we resolve remaining ambiguity?

AoA Estimation Framework – An Example (cont.)

• Estimate angles for Route 2
• With

, we get

• Subtracting 30° from

, we get

• Comparing with

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AoA result

Special Case: Dominant Reference Source

• Revisiting power spectral density
• For a dominant reference source,
• Therefore, unknown AoAs can be measured directly as

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Set of peaks in the spectrum

Experimental Results – Active Source AoA Estimation

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True AoAs Estimated AoAs {66.42∘, 120∘} {67.74∘, 120.22∘} {66.42∘, 120∘} {66.14∘, 120.29∘} {66.42,∘ 120∘, 143.1∘} {65.99∘, 117.4∘, 140.72∘} {90∘, 120∘} {90.43∘, 117.57∘} MAE 𝟐. 𝟒∘

Experiment areas True and estimated angles Experiment details: WiFi 802.11n measurements Transmitter: TP-Link AC1750 router Receiver: Intel 5300 WiFi NIC

Experimental Results – Passive Source AoA Estimation

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True AoAs Estimated AoAs {90∘, 120∘} {92.38∘, 124.2∘} {90,∘ 110∘} {89.14∘, 111.4∘} {45∘, 90∘} {42.18∘, 80.14∘} {45∘, 69∘, 90∘} {46.68∘, 64.88∘, 86.66∘} {90∘, 110∘} {89.14∘, 111.4∘} MAE 𝟑. 𝟘𝟘∘

Experiment areas True and estimated angles Experiment details: WiFi 802.11n measurements Transmitter: TP-Link AC1750 router Receiver: Intel 5300 WiFi NIC

Target Tracking Framework

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• For a single moving active target
• Comparing with the AoA estimation

model,

• Hence, we can measure
• Similarly, for a moving passive target

Direct signal from Txfix Phase at 𝑢 = 0 Active moving target Passive moving target

Ambiguity in Tracking

• Consider a moving active target
• Momentarily assume knowledge of location

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Measurement of at one Rx Measurement of at two Rxs

Need to incorporate motion dynamics to resolve the remaining ambiguity

Particle Filter for Tracking

• receivers located at
• Fixed transmitter located at
• State at time
• Measurement process at time
• A constant-speed motion model for

dynamics of

• Measurement equation relates

to state

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Tracking simulation with PF

Experimental Results – Active Target Tracking

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MAE = 𝟒𝟏. 𝟕 cm Experiment area Tracking result – Route U

8 m x 8 m area WiFi 802.11n transceivers

Experimental Results – Tracking a Passive Robot

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Experiment areas Tracking results

MAE = 𝟑𝟒. 𝟑𝟖 cm

8 m x 8 m area 8 m x 8 m area

Experimental Results – Human Tracking

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MAE = 𝟒𝟏. 𝟑𝟕 cm Experiment area Tracking result

Comparison with State-of-the-Art

• AoA estimation
• Existing approaches need phase
• Comparing case of active sources:

▪ Our approach achieves MAE of 1.76° ▪ Typical phase-based approaches achieve comparable MAE of 0.93°

• Target tracking
• Several existing methods require phase, complex transceivers
• Magnitude-only approaches typically need several transceivers

and/or high computation and/or extensive calibrations.

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Conclusions

• New framework for magnitude-based AoA estimation
• Using spectral content of autocorrelation of signal magnitude
• Angular localization of active and passive sources
• MAE of 2.44° over all the experiments
• Target tracking
• Duality of AoA framework
• Nonlinear dynamical system modeling and particle filter
• Using only three receivers and one transmitter
• Decimeter-level target tracking of active and passive targets

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Thank you!

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This work is funded by NSF CCSS award # 1611254