4. Localization & Tracking Perspective

From Positioning to Tracking

Traditional positioning often estimates a static location. ISAC goes further: it can estimate position, velocity, direction, and trajectory over time.

Tracking is valuable because communication links are dynamic. If the network knows where a user is moving, it can predict the next beam instead of waiting for the link to fail.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Device-Based Localization

Device-based localization estimates the position of a connected user. The user participates in the protocol and may send pilots or feedback.

Measurements include:

• Time of arrival.
• Angle of arrival.
• Angle of departure.
• Doppler.
• Channel state information.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Device-Free Localization

Device-free localization tracks objects that do not carry a radio device. Examples include pedestrians, vehicles, drones, and human motion.

ISAC supports this because the radar-like echo can reveal object presence and motion even if the object is not connected to the network.

For device-free tracking, the object does not cooperate with the network. The base station must detect weak reflections, separate them from clutter, associate them across time, and update a trajectory estimate. This is much harder than simply locating a connected phone that can transmit pilots.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Doppler and Micro-Doppler

Doppler measures velocity along the radio path. Micro-Doppler comes from small moving parts, such as arms, legs, rotating blades, or vibrating machinery.

Micro-Doppler can help distinguish:

• Human walking vs vehicle motion.
• Drone rotor motion vs bird motion.
• Machine vibration patterns.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Predictive Beamforming

If sensing estimates user motion, the network can predict future beam directions. This is useful for high-speed trains, vehicles, UAVs, and mmWave links where beam misalignment causes fast outages.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Tracking Filters

Tracking usually uses temporal filters such as:

• Kalman filter.
• Extended Kalman filter.
• Particle filter.
• Bayesian multi-target tracking.
• Learning-based trackers.

The ISAC waveform provides measurements; the tracker converts them into stable trajectories.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Main Challenges

• Clutter from static reflectors.
• Multi-target association.
• Occlusion and blockage.
• Synchronization.
• Privacy.
• Real-time processing.
• Joint optimization with data communication.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.

Takeaway

ISAC localization is powerful because the network can both communicate with users and sense non-connected targets. The most important advantage is not just knowing where something is, but predicting where it will be next.

References for this section3

• F. Liu et al., "Integrated Sensing and Communications", IEEE JSAC, 2022.
• J. A. Zhang et al., "An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing", IEEE JSTSP, 2021.
• K. Li et al., "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems", IEEE TVT, 2024.