用于分布式传感的时延信号联合极值压缩与检测
Joint Extremum Compression and Detection of a Time-Delayed Signal for Distributed Sensing
浏览论文内容
中文总结 AI 辅助
研究分布式传感系统中联合压缩与检测问题，提出基于极值的低复杂度方案，推导其误报和漏检概率上界，仿真表明该方案优于基线和基准，接近信息论基准。
中文摘要 AI 辅助
我们研究分布式传感系统中的联合压缩与检测问题，受物联网网络中设备到设备连接以及分布式雷达等应用的推动。在这类系统中，空间分离的传感器必须在通过高带宽受限链路通信时，协同判断其观测是否源自共同的潜在信号。我们考虑一个基本模型，其中一个传感器（编码器）观测平稳带限高斯过程的连续时间实现，另一个传感器（解码器）观测该信号的延迟且有噪声版本，延迟未知。编码器仅向解码器传输\(k\)位消息以辅助进行二元决策：观测是统计独立的，还是同一信号的时移噪声版本。我们提出一种基于极值的低复杂度方案，利用信号结构在严格通信约束下实现可靠决策。我们推导了该方法误报和漏检概率的非渐近上界以及后者的简化渐近界。代表性仿真表明，该方案优于普遍的每样本1位量化基线和基于费希尔信息的压缩基准，同时接近信息论（不可实现）速率失真基准。
英文摘要
We study the problem of joint compression and detection in distributed sensing systems, motivated by applications such as device-to-device connectivity in IoT networks and distributed radar. In such systems, spatially separated sensors must collaboratively decide whether their observations stem from a common underlying signal, while communicating over highly bandwidth-limited links. We consider a fundamental, insightful model in which one sensor (the encoder) observes a continuous-time realization of a stationary bandlimited Gaussian process, while the other sensor (the decoder) observes a delayed and noisy version of that signal, with an unknown delay. The encoder is allowed to transmit only a $k$-bit message to the decoder to assist in making a binary decision: either the observations are statistically independent, or they are time-shifted noisy versions of the same signal. We propose a low-complexity extremum-based scheme that exploits the structure of the signal to enable reliable decision-making under tight communication constraints. We derive nonasymptotic upper bounds on the false alarm and mis-detection probabilities of our method, as well as a simplified asymptotic bound for the latter. Representative simulations demonstrate that the proposed scheme outperforms the prevalent 1-bit-per-sample quantization baseline and a Fisher-information-based compression benchmark, while closely approaching an information-theoretic (nonrealizable) rate-distortion benchmark.
