multi-object tracking with radar
Post on 12-Jun-2022
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Multi-Object Tracking with Radar
Karthik Ravindran
Nigam Katta
Agenda
2
1. Introduction to Target tracking
2. Radar sensor and what it measures
3. Kalman filter for single target tracking
4. Generalization to multiple targets
5. Addressing the Data association problem
6. Summary
Target tracking
3
Target tracking is the problem of estimating the kinematic parameters
(position, velocity etc.) of moving targets using sensor measurements
The number of targets can vary from one or to many
The sensor can itself be static or moving
Tracking is essential for environment perception in the context of autonomous
navigation
Tracking Illustration
4
Ego vehicle
sensor
Target 1
Target 2
Target 3
trajectories
Range-Bearing sensor
5
• Measures the radial distance and orientation (azimuth angle, elevation angle) of the target from the
sensor
Examples of Range-bearing sensor
(a) Radar
(b) Lidar
(c) Stereo camera
What the Radar measures
6
• In three dimensions, the sensor measures (a) range (b) azimuth (c) elevation (b) doppler
Radial velocity = doppler
What the Radar measures
7
• Multiple measurements (detections) for each target
Main components of a Tracker
8
Kalman filter
Data association
Kalman Filter
9
Uses the Range-bearing sensor measurements to estimate the positions and velocities of different targets
observed in the field of view of the sensor
Recursively estimates the kinematic parameters (position, velocity) at each time-step based on the sensor
measurements at each time-step and the previous estimates
Kalman Filter
10
Define State vector :
s = ( position, velocity, position, velocity, position, velocity … )
Define Measurement vector :
z = (range,azimuth,elevation,doppler, range,azimuth,elevation,doppler, range,azimuth,elevation,doppler … )
target 1 target 2
target 1
target 3
target 2 target 3
(position X, position Y, position Z)
(velocity X, velocity Y, velocity Z)
Measurement model
11
what we are interested in
what the sensor measures
( Measurement noise covariance matrix )
State transition model
12
( Process noise covariance matrix )
vehicle vehicle
Position 1 Position 2
(predicted)
Velocity V
Time T
Prediction-Correction steps
13
vehicle vehicle
Position 1 Position 2
(predicted)
Velocity V
Time T
vehicle
Position 2
(estimated)
vehicle
Position 2
(Radar observation)
Multiple measurements per target
14
Apply Kalman filter for each measurement (detection) and linearly combine the
individual estimates
.
.
.
.
{Detection 1}
{Detection 2}
{Detection n}
Kalman Filter 1
Kalman Filter2
Kalman Filter n
∑
p1
p2
pn
Estimated vehicle state
Tracking multiple targets
15
• Need to identify the number of targets – Clustering
• Each cluster represents a target
• Once clustered, for every scan the detections need to be mapped
to these targets – Data association
• Every time a new target comes into the FOV of the sensor, a new
cluster is created
Data association
16
• Map the detections to different targets
• Compute an association probability each target-detection pair
target
detection
d1
d2d3
d4
d5
d6
d7
d8
Target 1
Target 2
Target 3
MulticoreWare Confidential
Clutter
17
• Spurious detections from static targets (Road, Clouds, Sea etc.)
target
detection from target
Clutter detection
MulticoreWare Confidential
Data association methods
18
• Nearest neighbourhood method
• Probabilistic data association
• Joint probabilistic data association
MulticoreWare Confidential
Nearest Neighbourhood method
19
• Detections are mapped to its nearest target
• Does not discriminate clutter
MulticoreWare Confidential
Probabilistic data association
20
• Assumes a single target in the FOV
• Assumes a probabilistic model for the spatial distribution of clutter
• Computes an association probability for each detection
• Detections probable of being a clutter will assume smaller association probabilities
MulticoreWare Confidential
Joint probabilistic data association
21
• Assumes multiple targets in the FOV
• Assumes a probabilistic model for the spatial distribution of clutter
• Computes an association probability for each detection-target pair
• Detections probable of being a clutter will assume smaller association probabilities
p1p2
p3
p4p5
p6
p7p8
p9
p1, p2, p3 ….p9are theassociationprobabilitiesconsidering theother objects inthe scenario.
MulticoreWare Confidential
Summary
22
• Defined the Target tracking problem
• Radar sensor and its measurements (detections)
• Kalman filter for state estimation
• Generalization to multiple detections, multiple targets
• Clutter detections
• Data association methods
Thank you
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