In complex electromagnetic environments, quantifying the anti-interference capability of an indoor single radar chair requires a comprehensive evaluation combining the radar system's core indicators and the characteristics of the electromagnetic environment. Its anti-interference performance depends not only on the inherent characteristics of the radar hardware but also on the coordinated optimization of signal processing algorithms and electromagnetic protection design.
The core of anti-interference capability lies in the radar system's ability to suppress multiple types of interference. The radar module on an indoor single radar chair must cope with both active and passive interference. Active interference, such as frequency sweep jamming and pulse blocking jamming, suppresses radar echoes by emitting high-power noise signals. Passive jamming exploits electromagnetic reflectors in the environment to create false targets, disrupting radar signal analysis. When quantifying anti-interference capability, it's important to focus on evaluating the radar's target detection probability and false alarm rate in the presence of interference signals. For example, under the same interference power, whether the radar chair can maintain a target recognition accuracy rate of over 90% while keeping the false alarm rate below 5% is a key indicator of its anti-interference performance.
From a technical perspective, quantifying anti-interference capability requires integrating the radar signal processing process. Indoor single radar chairs typically utilize adaptive beamforming technology, dynamically adjusting the antenna array's weight coefficients to suppress signals from interfering directions. Quantitatively, this performance can be evaluated by calculating the interference suppression ratio: The improvement in the radar receiver's signal-to-noise ratio in the presence of an interference source directly reflects the optimization level of the beamforming algorithm. Spectrum sensing and dynamic frequency band switching capabilities are also important quantitative dimensions. The radar chair must be able to monitor the electromagnetic environment's spectrum distribution in real time and automatically switch to an unoccupied band when an interfering frequency band is detected. The response time and success rate of frequency band switching during this process are key parameters for quantifying its anti-interference flexibility.
In terms of hardware design, quantifying the radar chair's anti-interference capabilities requires attention to the performance of the antenna and feed system and the transmit and receive modules. A low-sidelobe antenna design reduces the probability of interference signals entering the receiver through sidelobes. This effectiveness can be quantified using the sidelobe level metric: for every 3dB reduction in sidelobe level, the power of the interference signal entering the receiver is halved. The transmit module's peak-to-average power ratio also influences anti-interference capability. High-power transmission can enhance the radar echo's signal-to-noise ratio, but this requires a balance between power consumption and electromagnetic radiation safety. The dynamic range and linearity of the receiver module determine its tolerance to strong interference signals. Every 10dB increase in dynamic range significantly improves the radar chair's ability to handle high-power interference.
At the software algorithm level, quantifying anti-interference capabilities requires a combination of signal processing and data fusion technologies. Indoor single radar chairs typically integrate multi-sensor data fusion, integrating data from radar, infrared, or visual sensors to improve target recognition robustness. Quantification can assess the improvement in target detection probability after multi-sensor fusion. For example, if the detection probability of a single sensor is 70%, can the fusion increase the detection probability to over 95%? This reflects the algorithm's effectiveness in compensating for interference. Furthermore, the application of machine learning algorithms can further optimize anti-interference performance. By training models to identify interference signal characteristics, active interference suppression can be achieved. Quantification can be performed by evaluating the model's classification accuracy in interference environments.
Electromagnetic compatibility design is another important dimension in quantifying anti-interference capabilities. Indoor single radar chairs require electromagnetic shielding and filtering to reduce interference from their own electromagnetic radiation on external devices while suppressing the impact of external electromagnetic noise on radar signals. Quantifying shielding effectiveness can be accomplished through electromagnetic compatibility testing: within a specific frequency band, the shield must attenuate electromagnetic waves by at least 40dB to ensure that the radar signal is not affected by external interference. Furthermore, the power module's anti-interference design is crucial. A filtering circuit is required to suppress conducted interference on the power line, thereby assessing the interference threshold of power supply noise on the radar signal.
Quantifying the anti-interference capability of an indoor single radar chair requires a multi-dimensional evaluation system, combining hardware metrics, algorithm performance, and electromagnetic compatibility test results to generate a comprehensive score. Standardized test scenarios simulate complex electromagnetic environments to quantify the radar chair's target detection, tracking, and recognition capabilities under various interference types, providing a reliable performance reference for practical applications.
