模拟退火法驱动的超宽带传感器网络非相干信号检测算法

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本文主要探讨了"基于模拟退火机制的超宽带传感器网络非相干信号检测"这一主题，发表在Wireless Personal Communications期刊（2013年卷71，第2125-2145页）。该研究的DOI为10.1007/s11277-012-0927-5，作者是Bin Li、Zheng Zhou、Weixia Zou和Xuebin Sun。文章于2012年11月24日在线发布，由Springer Science+Business Media New York出版。在当前的超宽带（UWB）传感器广泛应用的商业和军事领域，遇到的主要挑战之一是在强多径传播环境下实现相干信号检测。由于复杂的算法需求和硬件复杂性，这通常变得不切实际。为解决这一问题，作者提出了一种创新的方法，即利用模拟退火机制进行非相干信号检测。这种方法将UWB信号检测框架化为一个生物学上的可行策略，具有广泛的适应性，不仅适用于信号检测，还可推广到二元假设目标检测问题，即判断目标是否存在。作者通过构建特征表示，将信号检测转化为二维特征空间中的两类模式（目标或背景）分类问题。在这个框架下，他们通过监督学习得到一个优化决策边界，这个边界是在给定训练实例的基础上通过数值方法得出的。这种方法的优势在于简化了传统的处理过程，降低了对精确实时定位的需求，同时保持了较高的检测性能。模拟退火是一种启发式搜索算法，它模仿了物质冷却过程中晶体结构逐渐趋于稳定的过程。在信号检测中，它可以帮助寻找全局最优解，即使在搜索空间复杂且局部最优解较多的情况下，也能有效地跳出局部最优，达到全局最优解。因此，作者将模拟退火机制巧妙地应用于非相干UWB信号检测，以提高信号的抗干扰能力和鲁棒性。这篇研究论文为超宽带传感器网络提供了一种新颖的非相干信号检测策略，通过模拟退火机制实现了高效的目标检测，并且能够有效应对多路径环境带来的复杂性。这种方法的应用前景广阔，对于提升UWB通信系统的可靠性和实用性具有重要意义。

Simulated Annealing Mechanic Based Noncoherent Signal Detection 2129

the down-Nyquist rate is also feasible, due to the exclusion of parametric CIR estimation.

Accordingly, the received signal y(n) can be expressed as:

y(n) =



h(n) + w(n) H

w(n) H

(4)

where w(m) is the additive white Gaussian noise (AWGN) with zero mean and variance σ

The decision variable in classical ED is deﬁned as Y

= 

(n), and channel E

given by 

(n)/(Nσ

). It should be noteworthy that, the formulation in (4)alsorepresents

the binary-hypothesis target detection, i.e. the presence or absence of a target, and h(n) can

be viewed as the reﬂected waveform from a speciﬁc target [9]. In the noncoherent scheme

above, signal demodulation is to identify whether there is sufﬁcient signal power available in

current time bin. Apparently signal power can characterize the UWB multipath signals only

in a rather coarse way, resulting in uncompetitive detection performance [17,21,35]. In this

paper, we develop a novel noncoherent algorithm that may enhance detection performance

considerably.

3 Noncoherent Signal Detection

Careful observations on nature processes imply that biological activities can solve the prob-

lems encountered in a much efﬁcient way, and the achieved decisions are far superior to what

we can achieve with our current engineering knowledge and methods, especially for the non-

ideal situations in the presence of information limitations and uncertainties [35–37]. Inspired

by the appealing mechanics mentioned above, we deal with noncoherent detection as a state

classiﬁcation problem by using the powerful SA optimization. We establish a character rep-

resentation of the reﬂected UWB signals, and further extract certain quantitative features

from it. The received signals are then mapped into different two patterns in a representative

space [38].

3.1 Characteristic Representation

Given the observed signals consists of N samples, y(n) (n = 0, 1, 2,...,N − 1), ﬁrstly we

construct a covariance matrix A according to

A = y

y (5)

In order to exploit the more profound information of UWB multipath signals, we further

derive a symmetric matrix from A

N×N

B = A

A (6)

We denote the principal diagonal elements of B

N×N

N×1

, while the elements imme-

diately below this diagonal by

(N−1)×1

N×1

= diag(B) = B

(

i, i

)

, i = 0, 2,...,N − 1(7)

(

N−1

)

×1

= diag

(

)

= B

(

i + 1, i

)

, i = 1, 2,...,N − 1(8)

The characteristic spectrum of UWB signals can be now deﬁned as the linear correlation

function between

and

(2N−2)×1



(9)

123

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