Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation and limited detection accuracy. This paper proposes an Adaptive Spiking Gating Multi-Scale Liquid State Machine (ASG-MSLSM) for orbital maneuver detection based on variations in satellite orbital parameters. The method integrates multi-scale reservoir pools with different scale-dependent decay factors and Leaky Integrate-and-Fire (LIF) neurons to enhance multi-scale temporal feature extraction capability. A spiking gating network is designed to adaptively learn fusion weights for multi-scale features, replacing traditional fixed equal-weight fusion strategies. During training, weighted binary cross-entropy loss is employed to address class imbalance. Experimental results based on real satellite data demonstrate that the proposed method significantly outperforms baseline models in maneuver detection metrics, achieving higher recall, improving feature separability, and reducing both missed detections and false alarms. These results indicate that the proposed method provides a robust solution for orbital maneuver detection.

Low Earth orbit (LEO) maneuver analysis remains challenging because maneuver events are sparse and short in duration, while accurate analysis requires simultaneous maneuver detection, onset-time localization, and continuous three-dimensional velocity-increment (Δv) estimation from long multivariate tracking sequences. To address these challenges, an improved multi-task iSpikformer framework is proposed for the joint detection, onset-time localization, and three-dimensional velocity-increment estimation of low Earth orbit maneuvers from multivariate tracking time series. The model combines a local convolutional encoder, stacked spiking Transformer blocks, and task-specific output heads within a unified multi-task framework. Event-centered supervision, hard-negative optimization, and robust Δv regression are employed to improve sparse maneuver detection and continuous parameter estimation. For full-scene inference, predictions from overlapping windows are fused and converted into discrete maneuver events through boundary-aware event extraction and validation-based calibration. Unlike approaches that separately handle maneuver detection and parameter estimation or rely on conventional dense sequence modeling, the proposed framework jointly learns maneuver occurrence, onset location, and three-dimensional Δv from a shared temporal representation. Evaluation on a synthetic dataset of Starlink-like LEO trajectories showed that the proposed method achieved an F1-score of 0.9018, an onset-time MAE of 14.54 s, a component-wise Δv MAE of 0.0307 m/s, and a vector RMSE of 0.1302 m/s. Compared with representative baseline methods, the proposed method showed improved maneuver-detection performance and more accurate Δv estimation while maintaining comparable onset-localization accuracy. The inference-stride analysis further showed that inference time could be substantially reduced over a moderate stride range with limited changes in detection and estimation performance, whereas an excessively large stride reduced detection sensitivity. The proposed framework therefore provides a unified data-driven approach to event-level LEO maneuver analysis and demonstrates the applicability of spiking temporal modeling to joint maneuver detection and continuous orbital-parameter estimation.