第七章 调制方式的识别

Pattern recognition approach
• The modulation classification module is composed of two subsystems; • the first one is a feature extraction subsystem, which extracts the key features from the received signal; • the second subsystem is a pattern recognizer, which processes those features and determines the modulation type of the transmitted signal according to a pre-designed decision rule.
区分： AM、DSB
||
LSB、USB
（3）零中心瞬时相位非线性分量的标准差
σ dp
1 1 2 = [ ∑ ϕ NL ( i )] − [ C An ( i )> at C
An ( i )> a t
ϕ NL ( i )]2 ∑
其中
at
是归一化瞬时幅度的门限，当 An (i )
大于门限时，可以认为该时刻的数据属于强信号 段，否则属于弱信号段。在弱信号段，瞬时相位对 噪声很敏感。C 为强信号段中数据个数。
AMC applications (cooperative and non-cooperative communication )
• • • • Software-defined radio Cognitive radio Intelligent modem Surveillance and electronic warfare
AMC approaches
• Decision-theoretic • Pattern recognition
Decision-theoretic approach
• Decision-theoretic approach is based on the likelihood function, where the modulation classification can be deemed as a multiple-hypothesis test. • The decision-theoretic classifiers with maximum likelihood (ML) are optimal, but the corresponding close-form solutions either are unavailable or involve the numerical search of high computation complexity. • This approach is not robust with respect to the model mismatch in the presence of phase or frequency offsets, residual channel effects, and so on.
调制方式的识别
1. 研究意义
随着通信技术的发展，无线通信环 境日益复杂。通信信号在很宽的频带上 采用不同调制参数的各种调制方式。对 未知信号调制方式的识别可提供信号的 结构、信号源以及特性等有用信息，如 何有效地识别和监视这些信号，在军事 和民用领域都是重要的研究课题。
Automatic Modulation Classification (AMC)
PH =
| S ( i + N c ) |2 ∑
N c = fc N / f s
DFT。
S (i ) 是中频信号
s(n) 的
利用 PL 和 PH 区分：USB || LSB
No
γ
m ax
> t γ m ax
Yes
No
σ
ap
> tσ ap
Yes
σ
Yes
dp
> tσ dp
Yes No
PL > PU
No
s( t ) = a ( t ) cos[ω c t + φ ( t )]
ω c = 2πf c 为载波频率
θ ( t ) = 2πf c t + φ ( t ) 为载波的瞬时相位 φ (t ) 为相位调制信号
a(t )
为幅度或幅度调制信号
(1)调幅信号（AM）：
s( t ) = a ( t ) cos[ω c t + φ ( t )]
φ (t ) = 0
a ( t ) = [ 1 + r ⋅ m ( t )]
瞬时幅度： 瞬时相位：
A( t ) = 1 + r ⋅ m ( t )
θ ( t ) = 2πf c
非线性分量：
ϕ (t ) = 0
wenku.baidu.com
r = 0.5
m ( t ) = cos( 2πf m t )
(2) 调频信号有：
s( t ) = a ( t ) cos[ω c t + φ ( t )]
m(t ) ≥ 0 m(t ) < 0
m ( t ) = sin( 2πf m t )
(4) SSB信号：
) s( t ) = m ( t ) cos( 2πf c t ) + m ( t ) sin( 2πf c t ) ) s( t ) = m ( t ) cos( 2πf c t ) − m ( t ) sin( 2πf c t )
An ( i )> a t
∑ϕ
NL
(i ) ]
2
at
是归一化瞬时幅度的门限，当 An (i )
大于门限时，可以认为该时刻的数据属于强信号 段，否则属于弱信号段。在弱信号段，瞬时相位对 噪声很敏感。C 为强信号段中数据个数。
1 ϕ NL ( i ) = ϕ ( i ) − N
∑ ϕ (i )
i =1
N
)2 瞬时幅度： A( t ) = m ( t ) + m ( t )
2
( LSB ) ( USB )
ˆ 瞬时相位： θ ( t ) = 2πf c t + arctan⎛ m ( t ) ⎞ ⎟ ⎜ ⎟ ⎜ ⎝ m(t ) ⎠
( LSB ) ( USB ) ( LSB ) ( USB )
θ ( t ) = 2πf c t − arctan⎜ ⎜
Pattern recognition approach: feature extraction
• Geometry features: constellation diagram • Statistics feature: PDF, moments, cumulants • Time-domain features: instantaneous magnitude, instantaneous phase, instantaneous frequency • Transformation-domain features: Fourier transformation, Wavelet transformation
• Automatically identify the modulation types of the transmitted signals by observing the received data samples, which would be corrupted by the noise and fading channels. • It is an intermediate operation between the signal detection and the data demodulation.
当载频完全已知时，瞬时相位的非线性分量 ϕ (n) 等于瞬时相位 θ (n)
θ ( n) − θ ( n − 1) 瞬时频率： f ( n) = 2πTs
（需要解混叠）
对于模拟调制方式识别，需要以下四个特征参数
（1）零中心归一化瞬时幅度谱密度的最大值
γ max = max | DFT ( Acn ( i )) | / N
1 ϕ NL (i ) = ϕ (i ) − N
∑ ϕ (i)
i =1
N
区分：AM
|| DSB
（4）信号频谱关于载波频率对称性的度量
PL − PU P= PL + PU
PL
PH
是信号功率谱的下边带 是信号功率谱的上边带
N c −1 i =1
PL =
N c −1 i =1
| S ( i ) |2 ∑
σ aa =
1 N 1 2 (∑ Acn (i )) − ( N i =1 N
| Acn (i ) |) 2 ∑
i =1
N
因为2ASK信号零中心归一化的瞬时幅 度在+0.5,-0.5两个电平间变动，于是它的 绝对值是恒定的，不具有绝对的幅度信 息。而另一方面，4ASK调制信号同时具有 绝对和直接的幅度调制信息。因此可以作 为两种调制类型的判决特征。 区分：2ASK || 4ASK
a ( t ) = Ac
瞬时幅度： 瞬时相位：
φ ( t ) = k f ∫ m(τ )dτ
t −∞
A( t ) = Ac
θ ( t ) = 2πf c + k f ∫ m (τ )dτ
t −∞
非线性分量： ϕ ( t ) = k f
∫
t
−∞
m(τ )dτ
m ( t ) = cos( 2πf m t )
Decision-theoretic vs. Pattern recognition
• The pattern recognition methods may be non-optimal but simple to implement and can often achieve the nearly optimal performance if carefully designed. • The pattern recognition methods can be robust with respect to the aforementioned model mismatches.
2
其 中：
N 为采样点数
Acn ( i ) = An ( i ) − 1
A( i ) An ( i ) = ma
1 ma = N
∑ A(i )
i =1
N
γ
max
用于判断信号的幅度调制信息 。
区分： FM
||
AM、DSB、LSB、USB
（2）零中心瞬时相位非线性分量绝对值的标准差
σ ap
其中
1 1 2 = [ ∑ ϕ NL ( i )] − [ C An ( i )> at C
x (n)
× ×
低通滤波器 低通滤波器
y I (n) yQ (n)
A/D
fs
− 2 sin(ω cn n)
ω cn = 2πf c / f s 已知
瞬时幅度： A( n) =
2 2 y I ( n ) + yQ ( n )
⎛ yQ ( n ) ⎞ 瞬时相位： θ ( n) = arctan⎜ ⎜ y ( n) ⎟ ⎟ ⎝ I ⎠
Pattern recognition approach: pattern recognizer
• Decision tree • Neural network • Support vector machines
2. 模拟调制方式的识别
模拟调制方式主要包括：AM、FM、DSB、 LSB、USB等模拟调制类型。统一表示为
(3) DSB信号：
s( t ) = a ( t ) cos[ω c t + φ ( t )]
a(t ) = m(t )
瞬时幅度：
φ (t ) = 0
A( t ) = m ( t )
m(t ) ≥ 0 m(t ) < 0
⎧ 2πf c 瞬时相位： θ ( t ) = ⎨ ⎩ 2πf c + π
⎧0 非线性分量： ϕ ( t ) = ⎨ ⎩π
FM
DSB
AM
LSB
USB
模拟调制方式识别
3. 数字调制方式的识别
数字调制信号主要包括有: 2ASK、4ASK、2FSK、4FSK、BPSK和QPSK。 用于数字调制信号自动识别的特征参数除 了前面用于模拟调制信号识别的前3个参数 外，还需要再加上以下2个特征参数 :
（1）零中心归一化瞬时幅度绝对值的标准偏差
ˆ ⎛ m(t ) ⎞ ⎟ m(t ) ⎟ ⎠ ⎝
ˆ 非线性分量：ϕ ( t ) = arctan⎛ m ( t ) ⎞ ⎜ ⎜ m(t ) ⎟ ⎟ ⎝ ⎠
ˆ ⎛ m(t ) ⎞ ϕ ( t ) = − arctan⎜ ⎜ m(t ) ⎟ ⎟ ⎝ ⎠
信号的正交变换
2 cos(ω cn n)
x s (t )