Airborne AIIS receivers require long-term stable operation in maritime or aerial monitoring scenarios, and their power consumption and endurance directly determine their mission performance. To balance low power consumption and long-term operational performance, a comprehensive approach is needed, encompassing hardware design, communication protocol optimization, power management strategies, and energy harvesting technologies. The following analysis focuses on key technological pathways.
At the hardware level, low-power chips and circuit design are fundamental. Airborne AIIS receivers must utilize processors and RF front-end chips optimized for low-power scenarios. These chips typically integrate Dynamic Voltage and Frequency Scaling (DVFS) technology, which adjusts the operating voltage and frequency in real time according to the mission load, such as reducing the main frequency to a minimum during idle periods to reduce static power consumption. Simultaneously, employing high-efficiency power amplifiers (PAs) and low-noise amplifiers (LNAs) reduces energy loss during RF signal transmission and reception, while optimized printed circuit board (PCB) layout reduces parasitic capacitance and inductance along the signal transmission path, further reducing reactive power consumption.
Communication protocol optimization is the core of reducing dynamic power consumption. AIS devices utilize the Time Division Multiple Access (TDMA) protocol based on the VHF band. Airborne receivers can reduce unnecessary eavesdropping through intelligent time slot management. For example, using "wake-up over the air" technology, the receiver enters deep sleep mode during non-data transmission periods, only briefly waking up in a preset time slot or upon detecting a specific preamble. This method can reduce eavesdropping power consumption to less than three percent of traditional modes. Furthermore, an adaptive message length and transmission interval strategy is employed to dynamically adjust the data update frequency based on target density, avoiding energy waste caused by frequent communication.
Power management strategies require fine-grained energy allocation. Airborne AIIS receivers are typically equipped with rechargeable batteries or supercapacitors, requiring multi-stage power management units (PMUs) for efficient energy utilization. The first-stage power conversion uses a high-efficiency DC-DC converter to stabilize the input voltage at the chip's required level; the second-stage power supply isolates unnecessary modules through load switches, such as cutting off power to sensors and display units in sleep mode; the third-stage power supply utilizes low-dropout linear regulators (LDOs) to provide low-noise power to sensitive circuits. Simultaneously, an energy budgeting algorithm is introduced to dynamically adjust the device's operating mode based on remaining battery power. For example, when the battery level falls below a threshold, it automatically switches to a low-speed communication mode to extend mission duration.
Energy harvesting technology supplements long-term operation. For long-term deployed aerial payloads, solar panels or vibration energy harvesting devices can be integrated to convert ambient energy into electricity. Solar cells require lightweight, high-conversion-efficiency flexible thin-film materials to accommodate the curved surface mounting requirements of the payload; vibration energy harvesting utilizes piezoelectric ceramics or electromagnetic induction principles to convert mechanical vibration into electricity, suitable for flight scenarios with continuous vibration. Through the coordinated operation of energy harvesting and energy storage units, a "self-powered" system can be constructed, significantly reducing reliance on external charging.
Software algorithm optimization unlocks hardware potential. Low-power operating systems and task scheduling algorithms prioritize critical tasks and delay unnecessary computations. For example, an event-driven programming model is used, waking the processor core only when sensor data changes or communication requests are received; machine learning algorithms are used to predict target trajectories, reducing the number of full-band scans and lowering RF module operating time. Furthermore, optimized data compression and encryption algorithms reduce the computational load on processing units, thereby indirectly reducing power consumption. System-level collaborative design is crucial. Power consumption optimization for airborne AIIS receivers needs to be integrated throughout the entire lifecycle, from requirements analysis and architecture design to module development and testing. For example, in the requirements phase, clearly defining mission duration and data update rate metrics guides hardware selection and algorithm design; in the architecture phase, adopting modular design facilitates independent optimization of power consumption in each subsystem; and in the testing phase, verifying energy consumption performance under different operating conditions through real-world simulations allows for iterative optimization of power management strategies.
Improving the low power consumption and long-duration performance of airborne AIIS receivers relies on multi-dimensional collaboration among hardware, protocols, power supplies, energy harvesting, and software. By integrating low-power chips, optimizing communication protocols, implementing refined power management, supplementing environmental energy, and employing intelligent software algorithms, efficient and reliable monitoring systems can be built to meet the needs of long-term maritime or aerial missions.
