Passive radar (PR) relies on receiving signals reflected from targets by other existing noncooperative radiation sources, which are broadly divided into ground- and space-based categories, to achieve target detection and tracking. In the context of space-based PR, this paper proposes a PR using the Iridium satellite signal, which is a low-orbit satellite communication signal with global coverage. With an improved detection range and accredited ambiguity function, a PR using the Iridium satellite signal can address the issues of limited terrestrial coverage for ground-based PR and insufficient receiving power for medium- to high-orbit space-based PR. In addition, to address the problem of multipath clutter broadening caused by the dual movement of low-orbit satellites and the PR receiver, this paper proposes a nonuniform Doppler extraction-enhanced multichannel extensive cancellation algorithm (NuDE-mECA) to suppress multipath clutter broadening while preserving near-field low-speed target detection capabilities. Compared with the traditional multichannel ECA and uniform Doppler extraction multichannel ECA, the NuDE-mECA achieves significantly reduced computational complexity while improving clutter suppression performance and maintaining detection capabilities for low-speed targets. These results provide valuable insights for the lightweight and high-precision design of space-based PR systems.

Syntheticaperture radar (SAR) and optical (OPT) image fusion aims to leverage their complementary information to obtain a more comprehensive representation of ground objects. However, significant discrepancies exist between the two modalities in terms of imaging mechanisms and feature distributions. Consequently, existing multi-modal image fusion methods struggle to achieve robust cross-modal feature alignment and deep semantic consistency between the fused results and the source modalities. To address the above challenges, this paper proposes a correlation and semantic prior-guided multi-scale cross-modal interaction network (CSP-MCIN) for effective SAR-OPT image fusion. Specifically, CSP-MCIN first employs two modality-specific encoders based on ResNet-18 to extract low-level details and high-level semantic features from SAR and OPT images, respectively. Subsequently, a multi-scale interactive decoder integrating cross-modal Transformers and gated fusion units is constructed to align and aggregate semantic and detail information from both encoders. Finally, to strengthen source-modality representations, a novel loss function combining a pixel-domain correlation loss and a CLIP-guided semantic consistency loss is designed and optimized under a PCGrad-based multi-objective optimization scheme. Experimental results on public SAR-OPT image datasets demonstrate that the proposed CSP-MCIN achieves superior fusion performance and computational efficiency compared with state-of-the-art approaches.

Passive radar (PR) relies on receiving signals reflected from targets by other existing noncooperative radiation sources, which are broadly divided into ground- and space-based categories, to achieve target detection and tracking. In the context of space-based PR, this paper proposes a PR using the Iridium satellite signal, which is a low-orbit satellite communication signal with global coverage. With an improved detection range and accredited ambiguity function, a PR using the Iridium satellite signal can address the issues of limited terrestrial coverage for ground-based PR and insufficient receiving power for medium- to high-orbit space-based PR. In addition, to address the problem of multipath clutter broadening caused by the dual movement of low-orbit satellites and the PR receiver, this paper proposes a nonuniform Doppler extraction-enhanced multichannel extensive cancellation algorithm (NuDE-mECA) to suppress multipath clutter broadening while preserving near-field low-speed target detection capabilities. Compared with the traditional multichannel ECA and uniform Doppler extraction multichannel ECA, the NuDE-mECA achieves significantly reduced computational complexity while improving clutter suppression performance and maintaining detection capabilities for low-speed targets. These results provide valuable insights for the lightweight and high-precision design of space-based PR systems.

Compliant muscle–tendon mechanics can improve terrain adaptation in musculoskeletal robots, but heterogeneous terrains impose competing requirements on compliance, propulsion, foot clearance, and support transfer. This paper proposes Value-Gradient Generalist Design (VGGD), a framework for selecting one fixed physiological muscle–tendon parameterization for cross-terrain locomotion while preserving skeletal topology and muscle routing. VGGD searches a compact PCA-based manifold that coordinates bounded scale factors for muscle strength, contraction-velocity capacity, and passive elastic response. A design- and terrain-conditioned value proxy is learned from sampled latent designs and then optimized by proximity-regularized projected value-gradient ascent under a soft-worst objective. Independent proxy validation uses 50 random designs solely for calibration and 100 separately sampled test designs excluded from fitting and Stage-B optimization. On the 100-design test set, the proxy shows positive agreement with mean cross-terrain return (Pearson $r=0.580$, Spearman $\rho =0.565$) and worst-terrain return ($r=0.583$, $\rho =0.551$), with all bootstrap intervals above zero and Holm-adjusted permutation $p=0.0006$. A separate paired local-direction test uses 60 previously unused evaluation seeds and equal feasible-space perturbation radii; at the nominal, midpoint, and selected designs, the proxy-gradient direction agrees with improvements in mean and seed-wise worst-terrain rollout return. After design selection, the muscle–tendon parameters are fixed, and a terrain-aware controller is trained with variational information-bottleneck regularization and auxiliary expert distillation. Checkpoint-resolved evaluation records identify 216/300 successes and an overall mean distance of 12.14 m for the complete pipeline, compared with 57/300 and 6.20 m for nominal-body PPO. The three checkpoint success rates are 84%, 78%, and 54% for Proposed and 49%, 0%, and 8% for PPO; exact two-sided policy-level permutation tests yield $p=0.10$ for success rate and $p=0.20$ for mean distance. All methods receive the same nominal 100-million-step final-controller budget per training run, while the 10 M Stage-A budget and expert-pretraining costs are reported separately.

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems.

Estimating the relative position and velocity of micro aerial vehicles (MAVs) using visual signals is a critical issue in numerous tasks. However, traditional relative motion estimation algorithms suffer severely from non-Gaussian noise interference and have limited observability, making it difficult to meet the practical requirements of complex dynamic scenarios. To address this dilemma, this paper proposes a Multimodal Decoupled Spatiotemporal Adaptive Network (MDSAN). Designed for air-to-air scenarios, MDSAN achieves high-precision relative pose and velocity estimation of dynamic MAVs while overcoming the observability limitations of traditional algorithms. In detail, MDSAN is collaboratively composed of two core sub-modules: Modality-Specific Convolutional Normalization (MSCN) blocks and Spatiotemporal Adaptive State (STAS) blocks. Specifically, MSCN uses custom convolution kernels tailored to three modalities—visual, physical, and geometric—to separate their features. This prevents interference between modalities and reduces non-Gaussian noise. STAS, built on a state-space model, combines two key functions: it tracks long-term MAV motion trends over time and strengthens the synergy between different modal features across space. Adaptive weights balance these two functions, enabling stable estimation, even when traditional methods struggle with low observability. Furthermore, MDSAN adopts a full-vision multimodal fusion scheme, completely eliminating the dependence on wireless communication and reducing hardware costs. Extensive experimental results demonstrate that MDSAN achieves the best performance in all scenarios, significantly outperforming existing motion estimation algorithms. It provides a new technical path that balances high precision, high robustness, and cost-effectiveness for technologies such as MAV swarm perception.