Positioning and synchronization are fundamental problems before multiplatform signal coherence on a potential target, for the purpose of detection, in airborne distributed coherent aperture radars (ADCARs). Hence, in this paper, the identifiability analysis of positioning and synchronization errors, including platform position errors (PPEs), time alignment errors (TAEs), and initial phases (IPs), is investigated using calibrating scatterers separated in disjoint Doppler resolution bins. Firstly, the hybrid Cramér-Rao lower bounds (HCRLBs) of these three perturbations are derived in an assumed observation geometry determined by the platform formation and scatterer placement. Then based on application scenarios, we conduct identifiability analysis using the HCRLB tool in the following three different cases: In the PPE-absent case, with one scatterer, relative errors are identifiable, whereas their absolute ones are not. In the PPE-only case, at least three distributed scatterers exist to identify PPEs except for ill-posed observation geometries. In the ADCAR case, both the two previous conditions are required for the whole identifiability. We assert that relative errors are identifiable because of the extra degree of freedoms (DoFs) brought by different transmit/receive pairs. Moreover, the ill-posed geometries leading to PPEs unidentifiability, from hybrid Fisher information matrix (HFIM) and time difference of arrival (TDOA) perspectives, are also elaborated. Finally, numerical experiments validate our conclusions, and the scatterer-based calibration achieves signal coherence on transmitting and receiving.

In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.

This letter deals with the problem of simultaneously locating the single source and refining erroneous sensor positions in a distributed network with the use of ground receivers whose positions are known a priori. Besides the external time delay (TD) measurements from sensors to ground receivers, we also exploit the inter-sensor TD measurements with the aim of improving the positioning accuracy. The devised scheme introduces the coupling terms of source and sensor locations as nuisance parameters to enable the formulation of pseudolinear equations, and then the problem could resort to a two-stage weighted least squares estimator. The scheme is shown in both theory and simulation to be capable of reaching the Cramer-Rao lower bound under the small measurement noise and small sensor position errors.