面向未知环境机动辐射源的单无人机具身追踪方法OA

Passive tracking of uncooperative maneuvering radiation sources by a single unmanned aerial vehicle(UAV)using only received signal strength indicator(RSSI)measurements is of considerable strategic and engineering value for missions such as electronic reconnaissance,spectrum monitoring,and emergency search and rescue.However,existing RSSI localization methods rely heavily on environmental propagation priors and treat parameter estimation,state filtering,and path planning as isolated serial processes,leaving them unable to handle dynamic scenarios in which core propagation parameters such as transmit power and path loss exponent are unknown and the target keeps maneuvering.The inherent low spatial resolution of scalar RSSI from a single channel further intensifies the structural conflict between exploration and ex-ploitation.To address these challenges,this paper proposes an embodied intelligence-based closed-loop tracking method for a single UAV,which adopts structured uncertainty metrics as a unified carrier of cross-layer information flow and deeply couples perception,cognition,and decision-making through bidirectional propagation of uncertainty,forming a cooperative tracking architecture driven by closed-loop information flow.At the perception layer,a parameter prior inference network is built to extract the empirical distribution of propagation parameters from historical spatio-temporal observation sequences,providing data-driven initialization for the subsequent Bayesian inference.At the cognition layer,a dual-chain decoupled in-ference mechanism based on Rao-Blackwellized conditional decomposition is designed for the joint estimation of unknown propagation parameters and maneuvering states,in which the outer particle filter estimates large-scale fading parameters on-line while the inner adaptive interacting multiple model filter accurately infers maneuvering states of the target.A particle revival mechanism guided by effective sample size monitoring is further introduced together with a belief inheritance mech-anism to prevent parameter resampling from disrupting the continuity of state tracking.At the decision layer,to mitigate the exploration-exploitation conflict noted above,a hierarchical planning strategy fusing information gain with model predictive control is constructed,and a learnable ranking gating network is introduced to perform real-time utility evaluation over can-didate strategies including tracking,probing,and conservative options,allowing the UAV to adaptively transition from broad-area exploration to tight escort flying according to belief uncertainty.Simulation results show that the proposed meth-od outperforms the compared baseline approaches across all core metrics under cold-start conditions with completely un-known propagation parameters,and the tracking performance under the mode without manual calibration priors approaches and in certain metrics surpasses the ideal performance of several baselines operating with known parameters,which verifies the adaptability and robustness of the proposed architecture under degraded sensing conditions.