## 4 Discussion  
The proposed NSWOA exhibits strong adaptability for dynamic satellite selection in dense LEO environments. With future constellations expected to exceed 10,000 satellites, scalability becomes critical. In our experiments involving over 6,000 satellites, the average number of visible satellites remained around 22 per epoch due to physical constraints like elevation angle and signal strength. NSWOA leverages these constraints to reduce the search space before optimization, ensuring computational efficiency. Even under growing satellite densities, the algorithm can maintain real-time performance by adjusting the population size and iteration count, while its convergence remains stable across all tested configurations.  
For practical deployment, the proposed NSWOA is highly suitable for real-time implementation on embedded platforms such as UAVs. Although our experiments were conducted on a desktop CPU (27-12700P), the algorithm achieved an average runtime of just 0.78 s per epoch, demonstrating both high computational efficiency and low solution latency.  
Figure 6: Comparison of DGDOP and the number of satellites selected by all selection and the NSWOA Optimization. (**a**) Comparison of DGDOP values between NSWOA star selection results and all selection results. (**b**) The comparison of the satellite count between the NSWOA-selected subset and the all-satellite selection results.  
Figure 7: Comparison of positioning accuracy and computation time between all satellite selection and the NSWOA. (**a**) Comparison of positioning accuracy for 2185 epochs. (**b**) Comparison of computation time for 2185 epochs.  
For embedded applications, the number of agents and iterations can be further reduced with minimal impact on optimization quality, as confirmed by convergence analysis.  
To support real-time operation in resource-constrained environments, the algorithm's modular design plays a crucial role. It features a fixed-size archive and constraint-based filtering, effectively limiting memory usage. Additionally, the computational structure of NSWOA allows for parallelization and hardware-level optimization on heterogeneous SoC platforms. Specifically,  
* High-throughput tasks such as DGDOP computation and agent position updates can be offloaded to DSPs, GPUs, or FPGAs, supported by high-speed local memory;
* Control flow and decision logic are best handled by ARM Cortex-A/R cores;
* Data with frequent access patterns, such as the archive and position vectors, should be stored in on-chip SRAM or shared caches to reduce latency;
* SIMD/GPU thread-level parallelism is well suited for large-scale agent updates;
* To alleviate DDR memory pressure, intermediate results can be buffered using L2/L3 caches or tightly coupled memory (TCM).  
These architectural advantages indicate that with modest adaptation, NSWOA can be effectively deployed for onboard satellite selection in real-time applications, particularly on large UAV platforms equipped with heterogeneous SoC architectures.  
Future work will focus on real-time onboard experiments and flight testing to assess performance under real-world dynamics. Additionally, integrating 3D path estimation and inertial-assisted navigation is expected to improve robustness in high-maneuver scenarios. Deep Q-learning techniques, such as prioritized experience replay mechanisms, have also shown promise in enhancing control stability under time-varying signals [40]. Inspired by these methods, adaptive learning-based optimization strategies may be incorporated into the NSWOA framework to further advance intelligent navigation using non-cooperative LEO signals.