Gridless High-Order Target Kinematic Parameter Estimation in Passive Radars—From Cross-Ambiguity Function to Zak Transform

This article deals with practical considerations on signal processing in passive radars. First, the Zak transform is used to approximate the cross-ambiguity function (CAF) commonly applied in passive coherent location (PCL). Next, an efficient method for single-snapshot target kinematic parameter estimation is proposed. In contrast to state-of-the-art methods, the introduced technique is noniterative, numerically efficient, and has a constant computational cost. The presented approach to target kinematic parameter decomposition is compared to existing algorithms and thoroughly tested and validated in both simulations and real-life situations. A 5G-based passive radar demonstrator was used to estimate drone kinematic parameters for a representative example. As a result of signal decomposition, the maneuvering target has a narrower peak on the range-velocity (RV) map, streamlining its detection by accelerating the processing by several orders of magnitude.