Jammer Geolocation

Determines the direction of a jamming signal's wavefront as it impinges on an antenna array. By measuring the phase difference of the signal at multiple antenna elements, a line of bearing (LOB) is calculated. When multiple geographically separated sensors compute intersecting LOBs, the jammer's location is triangulated. This technique requires coherent, calibrated multi-channel receivers and is highly effective against narrowband and persistent jammers, though accuracy degrades in dense multipath environments.

Calculates a jammer's position by measuring the difference in time of arrival of the same signal at multiple synchronized sensors. Each time difference defines a hyperbola on which the jammer must lie; the intersection of multiple hyperbolae yields a precise location fix. TDoA requires extremely precise time synchronization (often via GPS-disciplined oscillators) between sensor nodes and is particularly effective against wideband or pulsed jamming waveforms where leading-edge detection is robust.

Estimates distance from a sensor to the jammer based on path loss, the attenuation of signal power over distance. Using a propagation model, RSS measurements from multiple sensors create circular loci of probable location. The intersection of these circles provides a position estimate. While the simplest method to implement—requiring only power measurements—it is highly susceptible to shadowing, multipath fading, and environmental variability, making it the least accurate standalone technique.

Exploits the Doppler shift observed when a sensor and a jammer are in relative motion. By measuring the frequency offset between the transmitted and received signals at multiple sensors, the relative velocity vector is determined. When combined with TDoA measurements, FDoA provides a hybrid geolocation solution that significantly improves accuracy for moving jammers or when sensors are deployed on airborne platforms. Requires highly stable local oscillators.

Modern systems employ deep neural networks to fuse heterogeneous measurements—AoA, TDoA, RSS, and FDoA—into a single, robust location estimate. These models are trained on high-fidelity ray-tracing propagation simulations to learn complex, non-linear relationships between signal features and transmitter location. This approach mitigates the weaknesses of any single technique, maintaining high accuracy even in non-line-of-sight (NLOS) urban canyons where classical geometric solvers fail.

Combining geolocation estimates with RF fingerprinting—the identification of a unique transmitter via hardware impairments—enables persistent tracking of a specific jammer even as it moves or ceases transmission. The fingerprint provides the data association key across time.

• Carrier frequency offset (CFO) and IQ imbalance serve as robust fingerprints
• Enables track-to-identity correlation in multi-jammer environments
• Deep learning models extract fingerprints from transient signal segments
• Fused output provides a labeled geospatial track for electronic order of battle