🤖 AI Summary
To address the challenge of detecting maneuvering small targets under low signal-to-noise ratio (SNR) conditions in mono-, bi-, and multistatic radar systems, this paper proposes a joint trajectory estimation and cross-coherent processing interval (CPI) long-term coherent integration method. By unifying the modeling of target motion, complex reflectivity, and clock offsets, the approach integrates extended Kalman filtering (or nonlinear optimization) with Neyman–Pearson detection to achieve time–frequency–phase coherence compensation under asynchronous multistatic configurations. Crucially, trajectory estimation is embedded directly into the long-term coherent integration framework for the first time, thereby overcoming conventional CPI duration constraints and enabling arbitrary-length cross-CPI integration. Simulation results demonstrate that the method successfully detects targets undetectable by conventional approaches under extremely low SNR, achieving significant improvements in detection probability and range estimation accuracy while maintaining a constant false alarm rate.
📝 Abstract
In this work, we consider the detection of manoeuvring small objects with radars. Such objects induce low signal to noise ratio (SNR) reflections in the received signal. We consider both co-located and separated transmitter/receiver pairs, i.e., mono-static and bi-static configurations, respectively, as well as multi-static settings involving both types. We propose a detection approach which is capable of coherently integrating these reflections within a coherent processing interval (CPI) in all these configurations and continuing integration for an arbitrarily long time across consecutive CPIs. We estimate the complex value of the reflection coefficients for integration while simultaneously estimating the object trajectory. Compounded with this is the estimation of the unknown time reference shift of the separated transmitters necessary for coherent processing. Detection is made by using the resulting integration value in a Neyman-Pearson test against a constant false alarm rate threshold. We demonstrate the efficacy of our approach in a simulation example with a very low SNR object which cannot be detected with conventional techniques.