New method senses fast distant targets in complex radio channels
Sensing Fast and Distant Targets in Overspread Channels with Zak-OTFS and Modular Arithmetic
Information Theory
Summary
Detecting faraway and fast-moving objects like drones or satellites using radio signals is tricky because their echoes blur together. The paper proposes a new way to send and analyze signals that breaks down this blur by combining multiple signal patterns and clever math. This approach helps identify each target clearly even when the signals overlap a lot. Their tests show the new method detects more targets with fewer mistakes than existing ways, especially over long distances.
What this means in practice
- •For radar system engineers: Improve detection of fast, distant targets in radar systems operating in complex, wide-ranging environments.
- •For drone communication developers: Enhance target identification accuracy in drone and airborne platforms communicating through challenging radio channels.
Authors
Andrea Novero, Mudasir Ahmad Sheikh, Danish Nisar, Saif Khan Mohammed, Michele Zorzi, Robert Calderbank
Abstract
Delay-Doppler (DD) modulations, such as Zak-transform-based orthogonal time frequency space (Zak-OTFS) modulation, represent the channel as a sparse set of delay and Doppler shifts, which makes them well suited for radar sensing. Sensing with a single DD frame, however, is unambiguous only when the channel spread factor, i.e., the product of the delay and Doppler spreads, is below one. Long-range mobility scenarios, including drones, airplanes, low Earth orbit (LEO) satellites, and underwater acoustic links, typically violate this crystallization condition, so that a single frame aliases the target delays, Doppler shifts, or both. Existing methods add a time-frequency pilot to a conventional DD frame and resolve the aliasing in only one dimension. We propose a frame design that combines multiple DD bases with different delay and Doppler periods, which together cover an overspread channel, on the same bandwidth and frame size. For two frames, we show that modular arithmetic on the wrapped peak positions uniquely recovers each target by pairing the peaks through their common projection and their gains, and we derive the condition under which this pairing is ambiguous. We implement the design with two superimposed Zak-OTFS frames and propose two receivers: dual-frame sensing (DS), which performs the joint detection directly, and DS with cross-pilot cancellation (DS-XC), which additionally cancels the cross-pilot interference, re-estimates the gains by joint least squares, and removes ghost targets. Both receivers keep a detection rate close to one as the spread factor grows, and DS-XC also achieves the lowest false-alarm rate and channel normalized mean squared error (NMSE). In a 100 km drone scenario, DS-XC detects 98% of the targets, compared with 81% for the best baseline, with a complexity that grows near-linearly with the time-bandwidth product for a fixed number of targets.