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.

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.

A computationally efficient target parameter estimation algorithm for frequency agile radar (FAR) under jamming environment is developed. First, the barrage noise jamming and the deceptive jamming are suppressed by using adaptive beamforming and frequency agility. Second, the analytical solution of the parameter estimation is obtained by a low-order approximation to the multi-dimensional maximum likelihood (ML) function. Due to that, fine grid-search (FGS) is avoided and the computational complexity is greatly reduced.

This work deals with the problem of joint source and sensor localization (JSSL) in an asynchronous sensor network via their time of arrival (TOA) connections, where some sensors are unknown-position transceivers with unpredictable clock offsets and the others are properly time-synchronized anchor receivers in exactly known positions. Apart from the information shared from unknown sensors to known anchors along with TOA measurements due to line-of-sight (LOS) and non-LOS propagation paths by the passive source, the gained TOA ones from both propagation paths associated with unknown sensor pairs also contribute to the JSSL process, where the presence of both transmitting and receiving time offsets simultaneously is taken into account. Starting from the TOA equation, we establish an optimization formulation via constrained weighted least-squares to identify positions of source and sensors by developing two localization solutions, one in closed-form and the other by semidefinite relaxation (SDR). The formulation enables the involvement of additional auxiliary variables in the closed-form solution and further allows the tightness from additional constraints in the SDR one. Both proposed solutions thus possess the better realization of global convergence than the previous work in terms of reaching the Cramer-Rao lower bound accuracy over the mild noise region. They are extended as well to accommodate whatever partial/none transceiver position observations or erroneous anchor positions are available. Simulations confirm the superiority of our methods against disappointed transceiver position observations in contrast to the previous work and performance advantages over the existing methods due to the integration of unknown transmitters as transceivers.

An open-loop coherent distributed array enables beam steering to arbitrary angles without feedback, provided that stringent electrical state alignment among distributed nodes can be achieved using perfect knowledge of range, range rate, and direction cosine relative to each node towards any assumed destination. When nodes are mounted on aircraft, the state-relying systematic parameters, including positions, velocities, and affiliated aircraft attitudes of node swarms, drift unpredictably over time, inducing dynamic registration errors in node ranges, range rates, and direction cosines, and further failure of coherent beam steering. To address this in practical air-launched vehicle applications with outliers, we develop a set of outlier-resistant extended Kalman filters (OREKFs) for calibration of multinode systematic parameters, which vary in terms of covariances of reweighted predictive state and observation noise based on various robust kernel functions. To circumvent parameter unidentifiability arising from array translation and rotation and pursue a higher level of calibration accuracy than nodes obtain independently, the measurements from internode paths and ground auxiliary transmitter (GAT)-node paths are combined along with shared information from the global positioning system. Recognizing that artificial bias may exist in reported GAT positions, one merges GAT position bias and measurement noise as equivalent observation noise, which allows filtering gain to account for inaccuracy of GAT positions. Space-time registration of mobile nodes is thus done based on online estimates of multinode parameters for coherence maintenance. Simulations confirm well with the theoretical developments.