To enhance the coherent synthesis efficiency of Distributed Coherent Aperture Radar (DCAR) under low-power calibration conditions, an adaptive subarray position calibration method based on Manifold Distance Estimation (MDE) and Symplectic Geometry Mode Decomposition (SGMD) is proposed. The method first employs manifold distance measures to approximate the local neighbourhoods of low-power calibration signals, thereby preserving the intrinsic structural information of the underlying signal manifold and deriving a trajectory matrix suitable for low-power calibration scenarios. The signal trajectory matrix is then embedded into a symplectic phase space, where Hamiltonian eigenvalues are obtained via symplectic similarity transformations. This process enables the accurate extraction of subarray spatial information from highly noise-contaminated calibration signals, effectively mitigating noise-induced distortion and improving the accuracy of DCAR coherent parameter estimation. Simulation and experimental results under low-power conditions indicate that when the received calibration signal has a Signal-to-Noise Ratio (SNR) of 12 dB, the proposed method can achieve at least 80% coherent synthesis efficiency in wide-area DCAR. For small-scale DCAR configurations, even with a reduced calibration signal SNR of 18 dB, the method still achieves an SNR gain close to the ideal performance level.

Image-goal navigation enables a robot to reach the location where a target image was captured, using visual cues for guidance. However, current methods either rely heavily on data and computationally expensive learning-based approaches or lack efficiency in complex environments due to insufficient exploration strategies. To address these limitations, we propose Bayesian Embodied Image-goal Navigation Using Gaussian Splatting, a novel method that formulates ImageNav as an optimal control problem within a model predictive control framework. BEINGS leverages 3D Gaussian Splatting as a scene prior to predict future observations, enabling efficient, real-time navigation decisions grounded in the robot's sensory experiences. By integrating Bayesian updates, our method dynamically refines the robot's strategy without requiring extensive prior experience or data. Our algorithm is validated through extensive simulations and physical experiments, showcasing its potential for embodied robot systems in visually complex scenarios. Project Page: www.mwg.ink/BEINGS-web.

Formation control is essential for swarm robotics, enabling coordinated behavior in complex environments. In this paper, we introduce a novel formation control system for an indoor blimp swarm using a specialized leader-follower approach enhanced with a dynamic leader-switching mechanism. This strategy allows any blimp to take on the leader role, distributing maneuvering demands across the swarm and enhancing overall formation stability. Only the leader blimp is manually controlled by a human operator, while follower blimps use onboard monocular cameras and a laser altimeter for relative position and altitude estimation. A leader-switching scheme is proposed to assist the human operator to maintain stability of the swarm, especially when a sharp turn is performed. Experimental results confirm that the leader-switching mechanism effectively maintains stable formations and adapts to dynamic indoor environments while assisting human operator.