Abstract —Radar emitter recognition plays an important role in military automated command and control system. It is a com p osite task that involves radar signal interce p tion, modulation recognition, features extraction and classification. In this p ap er, a wavelet p acket transform is used for feature extraction of unintentional modulation on pulse (UMOP). Then specific radar emitter recognition is achieved by a probabilistic SVM (PSVM). Exp eriments on simulation and actual radar data verify the correctness and validity of this method.
I. I NTRODUCTION HE radar emitter recognition seems to be the one of most difficult tasks in electronic support measures (ESM) and electronic intelligence (ELINT) systems. Specific radar emitter recognition can recognize different radars of the same type, which seems to be the highest level in modern
information warfare. Conventional recognition approaches,
which separate the received pulses into individual emitter group, are usually based on the use of pulse parameters such as direction-of-arrival (DOA), radio frequency (R F ), time-of-arrival (TOA), pulse width (PW), pulse repetition interval (PRI), called pulse descriptor word (PDW). These features are efficient in simple emitter environment. However, in modern radar systems, the threat environment has changed dramatically and more sophisticated waveforms have been used. To identify radar emitters in high density and high interleaving environment, we need to explore the detailed structure inside each pulse, called unintentional modulation on pulse (UMOP) [1]. The UMOP features provide important information to perform specific emitter identification (SEI) [2, 3], especially for radar emitters. Usually, radar interceptors search targets both in frequency domain and spatial domain. The received pulses of one certain radar are discontinuous and limited. An effectual method for emitter recognition is achieved by using individual pulse. As demonstrated [2] the features of waveform amplitude and frequency are combined to a feature vector. Then a linear discriminant analysis (LDA) based feature selection is utilized for choosing a subset of the original predictive variables. Anderson [4] uses the multiresolution analysis method to aid the classification of radar pulse in the Specific emitter identification (SEI) problem. Gillespie and Atlas [5] use the class-dependent time-frequency representations (TF Rs) for radar transmitter Manuscript received January 15, 2009.
L. Li, H.B. Ji and L. Wang are with the School of Electronic Engineering, Xidian University, Xi’an, 710071, PR China (e-mail: lilin@https://www.doczj.com/doc/684898885.html,; hbji@https://www.doczj.com/doc/684898885.html,; leiwang@https://www.doczj.com/doc/684898885.html,).
identification. F urthermore, many classifiers are applied to specific radar emitter recognition, such as neural network [6], attribute measure [7], support vector machines [8], etc. And in [9] minimum description length (MDL) criterion and competitive learning algorithm is used for online clustering of radar emitter.
In this paper, we propose a wavelet packet transform and probabilistic SVM-based specific radar emitter recognition. The remainder of this paper is organized as follows. Section II
presents various types of UMOP features and the radar signal
model. Section III discusses the specific radar emitter recognition, including preprocessing, feature extraction and classification. Section IV is the experiments and results. And then the conclusions are drawn in Section V. II. T HE UMFO F EATURES The UMOP features of radar emitter signals represent the characters of individual radar accurately. The extraction of UMOP features is the key problem of specific radar emitter recognition. The UMOP features can be classified as follows: 1) Frequency drifting Because of the difference in process, ageing and temperature, the output frequency of frequency source is different from the nominal frequency. And the output frequency may drift in the pulse [10]. When radar works at different frequency, the relative deviation is invariable, and the absolute deviation varies with the working frequency. So the frequency drifting is unique and stationary, and it can be regarded as the UMOP feature for radar signals with the same type and modulation. 2) Pulse envelope Pulse modulator is a key device for pulsed radar system. The ideal pulse envelope is a rectangle with stable frequency. However, because of the inevitable spurious parameters, such as distribution capacitance and lead inductance, and the continual varying of work voltage and current, the output envelope of the pulse modulator is not a rectangle [11]. Pulse modulator will generate stable and specific form of pulse envelope. The pulse envelope is invariant with the working frequency. 3) Phase noise Phase noise can be measured in the frequency domain, and is expressed as a ratio of signal power to noise power in a
1Hz bandwidth at a given offset ωΔ from the center
frequency 0ω, which is defined as follow [12] Specific Radar Emitter Recognition Based on Wavelet Packet
Transform and Probabilistic SVM
Lin Li, Hongbing Ji, Member, IEEE , and Lei Wang
T
Proceedings of the 2009 IEEE
International Conference on Information and Automation
June 22 -25, 2009, Zhuhai/Macau, China
{}()0,110log sideband total carrier P Hz L P ωωω+Δao
Δ=?????
(1) Because of the phase noise, there are sidebands around the
center frequency 0ωof the output spectrum of an oscillator. Suppose the unintentional phase modulation is ()sin 2n t f t ?απ=. Where α is the modulation index, n f is offset frequency. Then the output signal can be written as follow: ()()()()()
000()sin 2sin 2sin 2cos sin 2cos 2sin sin 2n n n U t A f t f t A f t f t A f t f t παππαππαπ=+=+ (2) Using Bessel function expansion for (2), under the
condition of 1α<<, we can obtain
()()
()000()sin 2sin 2()2
sin 2()2
n n A
U t A f t f f t A f f t αππαπ≈+++? (3)
So the phase noise ()t ? will cause two sidebands at
0f f ±. This result is consistent with (1).
4) Spurious output
There are various spurious outputs in the radar waveform accompanied with the emission of useful signal [13]. F or example, because of the use of nonlinear devices, the frequency synthesizer always generates many spurious frequency components [14]. Suppose the input signals are ()111sin s a t ω= and ()222sin s a t ω=, we can obtain the
output signal
()()()()()
()()
()2
3
011221231222021211122222112221212121233311221
sin sin 21
cos 2cos 22
cos()cos()1
sin 3sin 34
out s k k s s k s s k s s k k a a k a t a t k a t a t k a a t a a t k a t a t ωωωωωωωωωω=+++++++=++++?++??+?++"
" (4)
Except for the useful signals 12()ωω+and 12()ωω?, there are many spurious signals, such as 12()m n ωω+, 0,1,2,;0,1,2,m n =="".
Besides the frequency synthesizer, the radiation, crosstalk and leakage of frequency sources in the emitter, power ripple and vibration will cause different kinds of spurious outputs. Although these spurious outputs can be reduced by different filters, a unique and stationary spurious signal will be generated because of the specific devices and coupling circuit.
F rom the analysis above, the UMOP features consist of envelope, frequency and phase modulations. Therefore, the k th received pulse of one radar can be expressed as
0()()exp{(()())}()k k k I k U k k x t A a t j t t n t τφτφτφ=??+?++ (5) Where, 1,,P k N ="?0,1,,1t N =?". N is the signal length, k A is the amplitude of the pulse, ()a t is the specific envelope shape of this radar and k τ is the time delay. ()I
t φ
represents the intra-pulse intentional modulation, such as linear frequency modulation (LF M), binary phase shift keying (BPSK), frequency shift keying (F SK), etc. ()U t φ
represents the intra-pulse unintentional modulations. And ()k n t are narrowband gauss noises.
III. S PECIFIC R ADAR E MITTER R ECOGNITION
F ig.1 shows the flow chart of the specific radar emitter recognition in this paper. As the development of electronic technical, modern radars usually have various intra-pulse intentional modulations with different modulation parameters. The UMOP features may vary with the intentional modulation. So the intentional modulation classification and normalization should be achieved in the preprocessing process. The specific radar emitter recognition is executed for radar emitters with the same type of waveform and parameters. Then the feature extraction is achieved by wavelet packet transform and the maximum selection of decomposition bi-tree. F inally, the specific radar emitter classification and recognition is accomplished by the method of probabilistic SVM.
A. Preprocessing
Intentional modulation recognition is the foundation of specific radar emitter recognition. A number of methods have been proposed for modulation recognition of radar signals such as Ref. [15, 16].
Normalization is another important task for specific emitter recognition. It can avoid the influence of common parameters to simplify the recognition process. For pulses with the same modulation type, specific emitter recognition is affected by signal amplitude, delay and frequency, which are corresponding to amplitude normalization, time normalization and frequency normalization, respectively. As shown in Fig.2, because of the background noise and
DOA, the pulse energies differ with each other greatly, even two sequential pulses. F irst the amplitude normalization limits the amplitude of pulse to [1,1]? with linear or non-linear transform.
Samples A m p l i t u d e
(a)
Samples A m p l i t u d e
(b)
Fig.2 Two sequential pulses received from one radar
Time normalization, viz. pulse aligning should be
accomplished before frequency normalization. Here, we use a
correlative method according to the following procedure [17]:
1. Rank the pulses into a descending order k x , 1,
,P k N =" based on signal-to-noise ratio (SNR).
2. F or 2,
,P k N =", calculate ()()()j j k t
c x t x t ττ=?|, where 1,2,,2N τ=" an
d 1,2,,1j k =?".
3. Time shift of the k th pulse is given by
11
?arg max ()k k j j c τ
ττ?==|
4. Shift the k th pulse circularly ?k τ samples.
Frequency normalization is achieved by the cancellation of intentional modulation parameters, viz. demodulation. After the three normalizations above, we obtain the UMOP signal
()()exp{()}
()k U k z t a t j t n t φ′≈+ (6)
Because all the intentional modulation parameters of the pulses to be processed in Fig.1 are the same, so the parameters estimation errors can not influence the recognition of specific radar emitters.
B. Wavelet packet transform Wavelet transform, also known as multiresolution analysis is linear time-frequency analysis method. The Morlet-Grossmann definition [18] of the continuous wavelet transform (CWT) for a 1-D signal 2()()x t L R ∈ is
(,)()t b W a b x t dt a ?+∞??∞
?§·=¨??1
(7) where (
)t ? is the analyzing wavelet, (0)a > is the scale parameter and b is the position parameter. It is computationally impossible to analyze a signal using all wavelet coefficients, so one may wonder if it is sufficient
to pick a discrete subset to be able to reconstruct a signal from the corresponding wavelet coefficients. This method is the
discrete wavelet transform (DWT).
The orthonormal basis set in 2()L R of the DWT can be
represented as
2,(,)2()()2j j n j j n Z t k t φ∈-??°°
=??°°ˉ? (8) The reconstruction of any signal x of finite energy can be achieved by the formula
,,(
),()j n
j n j n x t x t φ
φ∞∞
=?∞=?∞
=
￠2|| (9)
DWT is conventionally achieved by repeating application of two filtering operations, high frequency detail superimposed on low-frequency components. The decomposition process is repeat performed to the low-frequency sub-band to compose the next level of the hierarchy. As shown in F ig.3, the discrete wavelet packet transform (DWPT) is an extension of DWT, which repeat the decomposition both in the low-frequency and the high-frequency sub-bands. A number of methods based on DWPT have been proposed for signals feature extraction,
such as Ref. [19, 20, 21]. The DWPT can be represented as
122(2)(2(2)j j n n u
w t k h u k t u ??=?? (10) 1212(2)(2(2)j j n n u
w t k g u k t u ?+?=?? (11) where j is the scale index, k is the translation index, h is the lower-pass filter and the is a high-pass filter with ()(1)(1)r g k h r =??. 1
U 1
1U
2U 2
2
U 32
U 0
3U 13U 23U 33U 4
3U 53U 6
3U 7
3U 0
2U 0
1U
Fig.3 The diagram of three levels wavelet packet transform
Let the length of a sampled signal is 02n N =, the
decomposition sub-spaces by the mirror filter can be written as
221111
00,,,0,,2k k k j j j j U U U j n k +?++=⊕=="" (12) We can obtain 02n j ? complete orthogonal basis vectors of
the sub-pace k j U by the mirror filter. The orthogonal basis set can be represented as {},()j k u n , where ,,j k n u is called
wavelet packets or wavelet atoms.
The essential of DWPT is that it splits the signal to time-frequency localized information by a filter process, and it is possible to combine the different levels of decomposition in order to achieve the optimum time-frequency representation of the original signal. The wavelet packet energy is very useful features for signal and image recognition. The wavelet packet energy can be calculated as
2,,211()N
j k j k n E u n N ==| (13)
C. Probabilistic SVM
SVM was proposed as an effective machine learning approach by Vapnik and Cristianini [22, 23]. This method is a statistical learning method with a good performance, and it is easier to implement than neural networks. The SVM method was successfully applied to various problems including signal classification, modulation recognition, image recognition, etc.
A SVM is used for classification of only two-class problems. A separating hyperplane is obtained by calculating the maximum distance to the closest data points of the training set. These closest data points are called as Support Vectors (SVs). Usually, the data points cannot be linearly separated. In this state, these data points can be transformed to a high dimensional space by using a nonlinear transformation. The nonlinear transformations are performed by using variable kernel functions, such as sigmoid, polynomial, linear, Gaussian radial basis function (RBF), etc. These kernel functions define an inner product in high dimensional space. A kernel function can be given as below:
(,)()()Kernel x x d x d x ′′=? (14)
where ()d x is a mapping from low dimension input space
to a high dimensional space for each input data point x . The
decision function of a SVM is given as:
1
()(,)K
i i i i i f x a y Kernel x x b ==+| (15)
where K is the number of data points, {1,1}i y ∈? is the class label of training point i x . i a is Langrangian multiplier and can be found by solving a quadratic programming
problem with linear constraints [22]. Constructing a classifier to produce a posterior probability is very useful in practical recognition situations [24, 25]. In radar context, the received pulse may be an interference signal or may belong to a new class. So, this pulse should be rejected which is not considered in the standard SVM. F urthermore, posterior probabilities are also required for decision fusion with other classifiers. F irst, Vapnik uses a method which maps the output of SVMs to probabilities by decomposing the feature space into a direction orthogonal to the separating hyperplane. In [24], Platt suggests a method for fitting the posterior probabilities (1)P y f = with a parametric model directly. It uses a parametric form of sigmoid:
1
(1)1exp()
P y f Af B ==
++ (16)
As long as 0A <, the monotonicity of (16) is assured. Here it assumes that the output of the SVM is proportional to the log odds of a positive example. The parameters A and B are found by minimizing the negative log likelihood of the training data, which is a cross-entropy error function:
min log()(1)log(1)i i i i i
t p t p ?+??| (17)
where i t are defined as
1
2
i i y t +=
(18) and i p are the output probabilities defined as
1
1exp()
i i p Af B =
++ (19)
The minimization in (17) is a two-parameter minimization problem, and it can be performed using any number of optimization algorithms.
After the process of probabilistic SVM, we can obtain the posterior probability of radar emitter recognition. The next task is to estimate the generalized confidence. We suppose that the posterior probability of a radar pulse belonging to the k th class is ()k P H (1,2,,)k C =!, where C is the class number of the training set. ()k P H ′ are the posterior
probabilities by descending sorting of ()k P H . 1()P H ′ and 2()P H ′ are the maximum and the second maximum posterior
probabilities, respectively. There are several methods for generalized confidence estimation based on posterior probability, such as:
Method 1. Use the maximum posterior probability, viz.
1()()s F x P H ′= (20)
Method 2. Use the difference between the maximum and
the second maximum posterior probabilities, viz. 12()()()s F x P H P H ′′=? (21) Method 3. Use the normalized difference between the
maximum and the second maximum posterior probabilities, viz.
()121()()()()s F x P H P H P H ′′′=? (22) Method 4. Use the negative entropy of all C posterior probabilities, viz. 2()()log ()s k k k F x P H P H ′′=| (23) Method 5. Use the selective measurement, viz. ()11()()1()s k k F x P H P H ≠′′=?∏ (24) F or the five generalized confidence estimation methods, we set different reject thresholds. If the generalized confidence is less than the threshold Th , the pulse required for classification is regarded as an unknown class not belonging to the database.
(),(),s s
F x Th accepted
F x Th rejected ≥-?
<ˉ (25) The five generalized confidence estimation methods use different posterior probability information. So we can fusion the decision results by a voting scheme.
IV. E XPERIMENTS A ND R ESULTS
In this section, we discuss the performance of the proposed method by numerical experiments. First, the simulation data consists of three radars with sinusoid waveform. We generate 200 pulses for each one with different SNRs and initial phases. As shown in Fig.4.(a) and (b), the pulse envelope and frequency drifting are different form each other, but the differences are very slight. The sampling frequency Fs is 1.
Samples A m p l i t u d e
(a)
-3
Samples F r e q u e n c y ( x F s )
(b)
Fig.4 The pulse envelope and frequency drifting of the three radars
Usually, the spurious output and the phase noise have same UMOP characteristics. Here, we use a sinusoid interference ()sin 2n t f t ?απ= to simulate the phase noise or the spurious output. The parameters are [0.011, 0.012, 0.0013]α= and [0.01, 0.01, 0.01]n f =.
In this experiment, we use 120 pulses of each radar for
training and 80 for testing. Fig.5 shows the correct rate (CR)
versus the dimension reduced by principle component
analysis (PCA). The DWPT filter type is sym5 and the
decomposition level is 7. The SNR is 0 dB. The thresholds of the five methods for generalized confidence estimation introduced in Section III.A are set to {}0.5,0.3,0.6,0.2,0.3?. These reject thresholds have a direct relation with the recognition performance, which need to be selected according to the false-alarm probability. The principle of voting is that the minority is subordinate to the majority. It can be found that the correct rate tends to be stable while the dimension larger than 10. The experiment results in this section are the
average of Monte-Carlo experiments for 20 runs.
Dimensions
Fig.5 Correct recognition rate vs. dimensions
Table I shows the correct rates using different DWPT filters. The decomposition level is 7, feature dimension is 20. It can be found that the recognition performances of the eight DWPT filters are almost the same.
As shown in F ig.2, the actual received pulses are single-tone frequency signals. The sampling frequency is about 200 MHz. There are 7 radars with the same type and function. Each one has 50 pulses, and the total pulse number
is 350. As mentioned in Section II, because of the background noise and DOA, the pulses’ SNR differ with each other greatly, even two sequential pulses. The SNRs of these pulses are higher than 15 dB. In this experiment, the training set consists of 6 radars with 30 pulses for each one. So, the total pulses number of training set is 180. The testing set consists of the residual pulses, viz. 6 known radars with 20 pulses for each one and the unknown radar with 50 pulses. The correct
rates and false reject rates using different DWPT filters are shown in Table II, where the decomposition level is 8 and
feature dimension is reduced to 50 by PCA. The thresholds
for rejection are the same as the first experiment above. The
correct rate and false reject rate (FRR) are defined as
correct recognition samples
test samples except unknown classes
CR = 100%N N × (26)
FRR = 100%false rejected samples
test samples N N × (27)
respectively. From the experiment result, we can find that all of the eight DWPT filters can obtain good recognition performances. The correct rates are greater than 94%, and the false reject rates are less than 3%. TABLE I
C ORRECT R ATES U SING
D IFFERENT DPWT F ILTERS FOR S IMULATION
D ATA DWPT filter
type
SNR 10dB SNR 5dB SNR 0dB db3 87.6% 84.1% 78.3% db5 87.5% 84.5% 78.2% db7 87.4% 84.4% 77.9% sym3 87.6% 84.5% 78.4% sym5 87.5% 84.6% 78.3% sym7 87.5% 84.5% 77.7% coif1 87.4% 84.4% 77.8% coif2 87.3% 84.3% 77.2% We analyze three wavelet families: Daubechies (db), Symlets (sym) and Coiflets (coif).
V.C ONCLUSIONS
In this paper, we studied the application of discrete wavelet packet transform and probabilistic SVM for specific radar emitter recognition. Experiments on simulation and actual radar signals verify the correctness and validity of this method. Applied to different data, the performances of different DWPT filters are variable. But the differences of correct rates are slight. The reasonable thresholds for rejection are very important in the proposed method. In Section IV, we choose the thresholds mainly depend on experience. The method may be improved by an automatically learning algorithm for the thresholds selection. Furthermore, the selection of decomposition level of DWPT is another problem which is not considered in this paper. Higher decomposition level means that larger computational burden is needed, but the increase of correct rate is not always guaranteed. These problems need to be further investigated to improve the stability and validity of the proposed method.
A CKNOWLEDGMENT
The authors wish to thank the Editor and reviewers for their valuable comments and good suggestions.
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TABLE II
C ORRECT R ATES AN
D F ALS
E R EJECT R ATES U SING D IFFERENT DPWT
F ILTERS FOR A CTUAL D ATA
DWPT filter type CR FRR
db3 95.1%
2.5%
db5 95.1%
2.5%
db7 94.4%
2.7%
sym3 95.6%
2.4%
sym5 95.6%
2.5%
sym7 94.9%
2.5%
coif1 95.2%
2.4%
coif2 95.1%
2.6%
We analyze three wavelet families: Daubechies (db), Symlets (sym) and Coiflets (coif).
第六部分多普勒天气雷达原理与应用（周长青） 我国新一代天气雷达原理；天气雷达图像识别；对流风暴的雷达回波特征；新一代天气雷达产品 第一章我国新一代天气雷达原理 一、了解新一代天气雷达的三个组成部分和功能 新一代天气雷达系统由三个主要部分构成：雷达数据采集子系统（RDA）、雷达产品生成子系统（RPG）、主用户处理器（PUP）。 二、了解电磁波的散射、衰减、折射 散射：当电磁波束在大气中传播，遇到空气分子、大气气溶胶、云滴和雨滴等悬浮粒子时，入射电磁波会从这些粒子上向四面八方传播开来，这种现象称为散射。 衰减：电磁波能量沿传播路径减弱的现象称为衰减，造成衰减的物理原因是当电磁波投射到气体分子或云雨粒子时，一部分能量被散射，另一部分能量被吸收而转变为热能或其他形式的能量。 折射：电磁波在真空中是沿直线传播的，而在大气中由于折射率分布的不均匀性（密度不同、介质不同），使电磁波传播路径发生弯曲的现象，称为折射。 三、了解雷达气象方程 在瑞利散射条件下，雷达气象方程为： 其中Pr表示雷达接收功率，Z为雷达反射率，r为目标物距雷达的距离。Pt表示雷达发射功率，h为雷达照射深度，G为天线增益，θ、φ表示水平和垂直波宽，λ表示雷达波长，K表示与复折射指数有关的系数，C为常数，之决定于雷达参数和降水相态。 四、了解距离折叠 最大不模糊距离：最大不模糊距离是指一个发射脉冲在下一个发射脉冲发出前能向前走并返回雷达的最长距离，Rmax=0.5c/PRF, c为光速，PRF为脉冲重复频率。 距离折叠是指雷达对雷达回波位置的一种辨认错误。当距离折叠发生时，雷达所显示的回波位置的方位角是正确的，但距离是错误的（但是可预计它的正确位置）。当目标位于最大不模糊距离（Rmax）以外时，会发生距离折叠。换句话说，当目标物位于Rmax之外时，雷达却把目标物显示在Rmax以内的某个位置，我们称之为‘距离折叠’。 五、理解雷达探测原理。 反射率因子Z值的大小，反映了气象目标内部降水粒子的尺度和数密度，反射率越大，说明单位体积中，降水粒子的尺度大或数量多，亦即反映了气象目标强度大。 反射率因子（回波强度）： 即反射率因子为单位体积内中降水粒子直径6次方的总和。 意义：一般Z值与雨强I有以下关系： 层状云降水 Z=200I1.6 地形雨 Z=31I1.71 雷阵雨 Z=486I1.37 新一代天气雷达取值 Z=300I1.4 六、了解雷达资料准确的局限性、资料误差和资料的代表性 由于雷达在探测降水粒子时，以大气符合标准大气情况为假定，与实际大气存在一定的差别，使雷达资料的准确度具有一定的局限性，且由于雷达本身性能差异及探测方法的固有局限，对探测目标存在距离折叠及速度模糊现象，对距离模糊和速度模
二〇一年十月 课题小论文 题 目：线性调频（LFM ）脉冲压缩雷达仿真学院：专 业： 学生姓名：刘斌学号：年 级： 指导教师：
线性调频（LFM ）脉冲压缩雷达仿真 一．雷达工作原理 雷达是Radar （RAdio Detection And Ranging ）的音译词，意为“无线电检测和测距”，即利用无线电波来检测目标并测定目标的位置，这也是雷达设备在最初阶段的功能。典型的雷达系统如图1.1，它主要由发射机，天线，接收机，数据处理，定时控制，显示等设备组成。利用雷达可以获知目标的有无，目标斜距，目标角位置，目标相对速度等。现代高分辨雷达扩展了原始雷达概念，使它具有对运动目标(飞机，导弹等)和区域目标(地面等)成像和识别的能力。雷达的应用越来越广泛。 图1.1：简单脉冲雷达系统框图 雷达发射机的任务是产生符合要求的雷达波形（Radar Waveform ），然后经馈线和收发开关由发射天线辐射出去，遇到目标后，电磁波一部分反射，经接收天线和收发开关由接收机接收，对雷达回波信号做适当的处理就可以获知目标的相关信息。 假设理想点目标与雷达的相对距离为R ，为了探测这个目标，雷达发射信号()s t ,电磁波以光速C 向四周传播，经过时间R 后电磁波到达目标，照射到目标上的电磁波可写成： ()R s t C - 。电磁波与目标相互作用，一部分电磁波被目标散射，被反射的电磁波为()R s t C σ?-，其中σ为目标的雷达散射截面（Radar Cross Section ,简称RCS ），反映目 标对电磁波的散射能力。再经过时间R 后，被雷达接收天线接收的信号为(2)R s t C σ?-。 如果将雷达天线和目标看作一个系统，便得到如图1.2的等效，而且这是一个LTI （线性时不变）系统。 图1.2：雷达等效于LTI 系统 等效LTI 系统的冲击响应可写成: 1 ()() M i i i h t t σδτ==-∑(1.1)
线性调频信号产生方法研究 摘要：本文利用fpga与dac5686完成了线性调频信号产生电路的设计与实现，该方法降低了系统软硬件设计的难度，缩短了开发周期，并提高了设计的可靠性，具有较高的实用价值和良好的应用前景。文章分析了线性调频信号，给出了信号产生电路硬件设计和控制电路软件设计方案，并通过功能实现验证文中方法的有效性。abstract: a generation module of lfm signal based on fpga and dac5686 is designed and realized in this paper. this technique decreases the difficulty of hardware and software design of the system, reduces development cycle and improves design reliability, has higher practical value and good application prospect. lfm signal is analyzed, based on which signal generation circuit and software of control circuit design project is put forward, and the effectiveness of this method is verified through the function realization. 关键词：线性调频；信号产生；fpga；dac5686 key words: lfm；signal generation；fpga；dac5686 0 引言 为了能够探测远距离目标，同时又具备较高的距离分辨力，脉冲压缩雷达通常发射较宽脉冲的线性调频（lfm）信号，而在接收时进行脉冲压缩。因而，如何产生良好的线性调频信号，对于脉冲压
一． 线性调频（LFM ）信号 脉冲压缩雷达能同时提高雷达的作用距离和距离分辨率。这种体制采用宽脉冲发射以提高发射的平均功率，保证足够大的作用距离；而接受时采用相应的脉冲压缩算法获得窄脉冲，以提高距离分辨率，较好的解决雷达作用距离与距离分辨率之间的矛盾。 脉冲压缩雷达最常见的调制信号是线性调频（Linear Frequency Modulation ）信号,接收时采用匹配滤波器（Matched Filter ）压缩脉冲。 LFM 信号(也称Chirp 信号)的数学表达式为： 22() 2()()c K j f t t t s t rect T e π+= (2.1) 式中c f 为载波频率，()t rect T 为矩形信号， 11()0,t t rect T T elsewise ? , ≤? =?? ? （2.2） B K T = ，是调频斜率，于是，信号的瞬时频率为()22c T T f Kt t + -≤≤，如图 2.1 图2.1 典型的chirp 信号（a ）up-chirp(K>0)（b ）down-chirp(K<0) 将2.1式中的up-chirp 信号重写为： 2()()c j f t s t S t e π= （2.3） 式中， 2 ()( )j Kt t S t rect e T π= （2.4） 是信号s(t)的复包络。由傅立叶变换性质，S(t)与s(t)具有相同的幅频特性，只是中心频率不同而以，因此，Matlab 仿真时，只需考虑S(t)。以下Matlab 程序产生2.4式的chirp 信号，并作出其时域波形和幅频特性，如图2.2。
%%demo of chirp signal T=10e-6; %pulse duration10us B=30e6; %chirp frequency modulation bandwidth 30MHz K=B/T; %chirp slope Fs=2*B;Ts=1/Fs; %sampling frequency and sample spacing N=T/Ts; t=linspace(-T/2,T/2,N); St=exp(j*pi*K*t.^2); %generate chirp signal subplot(211) plot(t*1e6,real(St)); xlabel('Time in u sec'); title('Real part of chirp signal'); grid on;axis tight; subplot(212) freq=linspace(-Fs/2,Fs/2,N); plot(freq*1e-6,fftshift(abs(fft(St)))); xlabel('Frequency in MHz'); title('Magnitude spectrum of chirp signal'); grid on;axis tight; 仿真结果显示： 图2.2：LFM信号的时域波形和幅频特性
[键入文字] 2019 年时政：2 月21 日国内篇 1、脱贫攻坚三年行动开局良好我国农村贫困发生率降至1.7% 记者从国新办吹风会上获悉：2018 年我国减少农村贫困人口1386 万，连续6 年超额完成千万减贫任务，贫困发生率下降到1.7%，全国已有153 个贫困县宣布脱贫摘帽，2018 年预计还有284 个贫困县退出。 2、长江经济带环境污染犯罪可从重处罚 随着环境污染犯罪案件多发高发，执法司法机关普遍反映实践中存在着确定管辖 难、调查取证难、司法鉴定难、法律适用难等突出问题，亟待通过顶层设计予以解 决。 20 日，最高人民法院、最高人民检察院、公安部、司法部、生态环境部首次就办理环境污染刑事案件有关问题联合出台《关于办理环境污染刑事案件有关问题座谈会纪要》，要求统一法律适用和政策把握，依法准确有效惩治环境污染犯罪，形成各部门依法惩治环境污染犯罪的合力。 此次《纪要》也对长江经济带区域污染环境犯罪规定了相关从重处罚的情形，规定 对于发生在长江经济带11 省（直辖市）的跨省（直辖市）排放、倾倒、处置有放射性的废物、含传染病病原体的废物、有毒物质或者其他有害物质的，将从重处罚。 3、农业农村部要求做好斑海豹幼崽救护工作 近日，大连破获一起特大盗捕斑海豹案件。案件发生后，农业农村部要求严查此事 并务必做好斑海豹幼崽救护工作，同时派员会同辽宁省农业农村厅赶赴大连，就案件 处理和斑海豹救治情况进行指导和对接。 下一步，农业农村部还将指导协调地方渔业渔政部门，妥善做好斑海豹幼崽收容救 助工作，待专家和兽医联合评估认可具有野外生存能力后再分批放归野外。农业农村 部还将进一步加强海洋馆等繁育场所规范管理，切断可能的非法贸易链条，联合有关 1
多普勒雷达系统仿真
精品好文档，推荐学习交流 摘要 现代通信系统要求通信距离远、通信容量大、传输质量好，作为其关键技术之一的调制解调技术一直是人们研究的一个重要方向。本文以MATLAB为软件平台，充分利用其提供的通信工具箱和信号处理工具箱中的模块，对数字调制解调系统进行Simulink设计仿真，并且进行误差分析。 数字化正交数字化正交调制与解调是通信系统中十分重要的一个环节，针对不同的信道环境选择不同的数字化正交数字化正交调制与解调方式可以有效地提高通信系统中的频带利用率，改善接收信号的误码率。本设计运用Simulink仿真软件对二进制调制解调系统进行模型构建、系统设计、仿真演示、结果显示、误差分析以及综合性能分析，重点对BASK，BFSK，BPSK进行性能比较和误差分析。在实际应用中，视情况选择最佳的调制方式。 本文首先介绍了课题研究的背景，然后介绍系统设计所用的Simulink仿真软件，随后介绍了载波数字调制系统的原理，并根据原理构建仿真模型，进行数字调制系统仿真，最后对设计进行总结，并归纳了Simulink软件使用中需要注意的事项。本文的主要目的是对Simulink的学习和对数字调制解调理论的掌握和深化，为今后在通信领域继续学习和研究打下坚实的基础。 关键字：排通信系统，Simulink仿真，数字化调制解调，BASK，BFSK
精品好文档，推荐学习交流 ABSTRACT TheThe Modern communication systems require communication distance, large communication capacity, good transmission quality, as one of its key technologies modem technology has been an important direction for researchers. In this paper, MATLAB software platform, providing full use of its communications toolbox and signal processing toolbox module, digital modulation and demodulation system Simulink design simulation and error analysis. Modulation and demodulation is a very important part of the communication system, for different channel environment to select different modulation and demodulation system can effectively improve the spectrum efficiency in a communication system, improve the bit error rate of the received signal. This design using Simulink simulation software binary modulation and demodulation system modeling, system design, simulation demo showed that the error analysis and comprehensive performance analysis, focusing on the BASK, BFSK, BPSK performance comparison and error analysis. In practice, as the case may select the best modulation. This paper describes the background of the research, then describes the system design using Simulink simulation software, then introduced the carrier digital modulation system of principles, and build a simulation model based on the principle of digital modulation system simulation, and finally the design summary and induction Simulink software matters that need attention. The main purpose of this paper is to study and Simulink digital modem theory of mastery and deepening for the future to continue learning and research in the field of communication and lay a solid foundation. Key Words: queuing theory, demand management, telecom offices
49 多普勒测风激光雷达系统 1.研究背景 大气风场信息是一项重要的资源，精确可靠的大气风场测量设备可提高风电可再生能源领域的利用率，改进气候气象学模型建立的准 确性，增强飞行器运行的安全性，因此在风电、航空航 天、气候气象、军事等领域都有着重要的意义。 风场信息测量的手段主要分为被动式和主动式两大类。传统的被动式测量装置有风速计、风向标和探空仪，主动式测量装置有微波雷达、声雷达等。风速计和风向标只能实现单点测量，借助测风塔后实现对应高度层的风场信息检测，这类传统装置易受冰冻天气影响，测风塔的搭建和维护也需要花费大量的人力物力，还存在移动困难和前期征地手续复杂等问题；微波雷达以电磁波作为探测介质，由于微波雷达常用波长主要为厘米波，与大气中的大尺寸粒子（如云、雨、冰等）相互作用产生回波，无法与大气中的分子或气溶胶颗粒产生作用，而晴空时大气中大尺寸粒子较少，因此微波雷达在晴空天气条件下将出现探测盲区。另外，微波雷达还具备庞大的收发系统也导致其移动困难；声雷达与微波雷达测量原理相似，不同的是将探测介质由微波改为了声波。声雷达的探测方式使得在夜间和高海拔地区易出现信噪比降低的情况甚至无法测量。因此，迫切需要补充新型的风场测量手段替代传统测风装置实现大气风场信息的测量。2. 测风激光雷达系统 2015年，南京牧镭激光科技有限公司成功研制出国产化测风激光雷达产品Molas B300，该产品基于多普勒原理可实现40～300 m 风场信息测 ■ 黄晨，朱海龙，周军 南京牧镭激光科技有限公司 第一作者 黄晨 量，风速测量精度可达0.1 m/s ，风向测量精度可达1°，数据更新率为1 Hz ，风速测量范围可达0～60 m/s 。测风激光雷达定位为外场应用装备，对环境适应性有较高要求，Molas B300可在外界温度范围为-40℃～50℃，相对湿度为0%～100%的环境条件下正常工作。除此以外，Molas B300体积小质量轻（约50 kg ）方便运输安装便捷，可显著降低项目前期施工时间。测风激光雷达采用激光作为探测介质，可与空气中微小颗粒发生相互作用，具有时空分辨率高、自动化程度高、安装简单易维护、移动便携性好等优势，可有效提高项目实施效率， 因此成为了最具前景的风场信息测量手段。 表1 各类风场探测技术的优缺点 探测技术优势 劣势风速计、风向标较高的水平分辨率， 成本低单点测量微波雷达三维风场探测，测量距离 可达100 km 晴空条件下不能使用，体积庞大声雷达三维风场探测探测距离较近，易受大气环境影响 测风激光雷达 三维风场探测，晴空下仍 能测量，移动便携性好 图1 测风激光雷达Molas B300
第37卷，增刊 红外与激光工程 2008年9月 V ol.37 Supplement Infrared and Laser Engineering Sep. 2008 收稿日期：2008-07-04 作者简介：程永强（1981-），男，陕西渭南人，助理研究员，主要从事激光雷达及中高层大气方面研究。Email:chengyq@https://www.doczj.com/doc/684898885.html, 钠激光雷达在临近空间探测方面的最新进展 程永强，胡 雄，徐 丽，闫召爱，郭商勇 （中国科学院空间科学与应用研究中心，北京 100190） 摘要：介绍了Na 激光雷达的研究背景，详细阐述了Na 激光雷达的国内外发展动态和Na 层测风测温激光雷达的基本探测原理，最后简要描述了发展Na 层测风测温激光雷达的重要意义。 关键词：激光雷达； Na 层测风测温； 临近空间 中图分类号：P4 文献标识码：A 文章编号：1007-2276(2008)增(激光探测)-0028-04 Advances of Na Lidar in near space detection CHENG Yong-qiang, HU Xiong ，XU Li, YAN Zhao-ai, GUO Shang-yong (Center for Space Science and Applied Research, Chinese Academy of Science, Beijing 100190, China) Abstract ：The research background of Na lidar is introduced, then the latest advances and the detecting theory of Na wind/temperature lidar is described in detail, at last the important significance of developing Na wind / temperature lidar is explained. Key words ：Lidar; Na wind / temperature lidar; Near Space 0 引 言 10 km 以上的地球大气称之为“中高层大气”[1] ， 它是日地系统的一个重要中间环节，包括对流层的上部、平流层、中间层和热层。目前国际上临近空间（20～100 km ）飞行器技术快速发展，临近空间因其飞行环境的特殊性和广阔的应用前景而受到高度关注。因此，中高层大气探测与研究在大气科学和空间物理中具有举足轻重的地位，在航天等高新科技领域也有重要的意义，也是优化人类的生存环境、保障人类社会可持续发展的需求。 光在大气中传播时，会被大气分子和气溶胶散射或吸收，这种大气分子对光的散射和吸收是使用激光对大气组成和特性进行探测的物理基础。目前，激光雷达(Lidar)是对中高层大气探测与研究的主要手段 之一[2]。根据激光束于大气物质相互作用机制，可设计不同的激光雷达[3]，包括气溶胶激光雷达、拉曼激光雷达、共振荧光激光雷达、瑞利散射激光雷达等。 共振荧光激光雷达利用共振荧光过程，将激发光的频率调至散射物质的吸收线，通过散射回波信号来探测大气特性。由于波源或观察者的运动而出现观测频率与波源频率不同的现象，称之为多普勒效应。利用这种多普勒效应进行测风的激光雷达称为测风激光雷达。多普勒频移的大小和方向由分子运动速度方向与激光束方向的夹角决定。因此，通过测量散射光的多普勒谱线频移量就可获得大气风速；通过测量散射光的多普勒谱线展宽就可获得大气温度。Na 层测风测温激光雷达[4]利用中层顶区域内的钠原子作为示踪物，由接收到的共振荧光散射回波光子数反演大气温度、风场及Na 原子数密度廓线。其仅有20多
实验一 雷达信号波形分析实验报告 一、 实验目的要求 1. 了解雷达常用信号的形式。 2. 学会用仿真软件分析信号的特性。 3了解雷达常用信号的频谱特点和模糊函数。 二、实验参数设置 信号参数范围如下： （1）简单脉冲调制信号： （2）载频：85MHz （3）脉冲重复周期：250us （4）脉冲宽度：8us （5）幅度：1V （2）线性调频信号 载频:85MHz 脉冲重复周期：250us 脉冲宽度：20us 信号带宽：15MHz 幅度：1V 三、 实验仿真波形 1.简单的脉冲调制信号 程序： Fs=10e6; t=0:1/Fs:300e-6; fr=4e3; f0=8.5e7; x1=square(2*pi*fr*t,3.2)./2+0.5; x2=exp(i*2*pi*f0*t); x3=x1.*x2; subplot(3,1,1);
plot(t,x1,'-'); axis([0,310e-6,-1.5,1.5]); xlabel('时间/s') ylabel('幅度/v') title('脉冲信号重复周期T=250US 脉冲宽度为8us') grid; subplot(3,1,2); plot(t,x2,'-'); axis([0,310e-6,-1.5,1.5]); xlabel('时间/s') ylabel('幅度/v') title('连续正弦波信号载波频率f0=85MHz') grid; subplot(3,1,3); plot(t,x3,'-'); axis([0,310e-6,-1.5,1.5]); xlabel('时间/s') ylabel('·幅度/v') title('脉冲调制信号') grid; 仿真波形： 0123x 10-4-101 时 间 /s 幅 度 / v 脉冲信号 重复周期T=250us 脉冲宽度为8us 1 2 3 x 10 -4 -1 1 时间/s幅度/v连续正弦波信号
巴克码—线性调频脉冲多普勒雷达matlab代码%% 雷达系统仿真 %% % 发射信号为13位巴克码和线性调频混合调制的信号，线性调频的中心频率为30MHz， % 调频带宽为4MHz，每一位码宽为10微秒，发射信号的帧周期为1毫秒 % 该雷达具有数字化正交解调、数字脉冲处理、固定目标对消、动目标检测(MTD)、 % 和恒虚警(CFAR)处理等功能 close all;clear all;clc; %%%%%%%%%%%%%%% 产生雷达发射信号 %%%%%%%%%%%%% code=[1,1,1,1,1,-1,-1,1,1,-1,1,-1,1]; % 13位巴克码 tao=10e-6; % 脉冲宽度10us fc=28e6; % 调频信号起始频率 f0=30e6; % 调频信号中心频率 fs=100e6; % 采样频率 ts=1/fs; % 采样间隔 B=4e6; % 调频信号调频带宽 t_tao=0:1/fs:tao-1/fs; % 调制信号，对于线性调频来说，调制信号就是时间序列 N=length(t_tao); k=B/fs*2*pi/max(t_tao); % 调制灵敏度，也就是线性调频的步进系数 n=length(code); pha=0; s=zeros(1,n*N); for i=1:n
if code(i)==1 pha=pi; else pha = 0; end s(1,(i-1)*N+1:i*N)=cos(2*pi*fc*t_tao+k*cumsum(t_tao)+pha); end t=0:1/fs:n*tao-1/fs; figure,subplot(2,1,1),plot(t,s); xlabel('t(单位:S)'),title('混合调制信号(13为巴克码+线性调频)'); s_fft_result=abs(fft(s(1:N))); subplot(2,1,2),plot((0:fs/N:fs/2-fs/N),abs(s_fft_result(1:N/2))); xlabel('频率(单位:Hz)'),title('码内信号频谱'); %%%%%%%%%%%%%%%%%%% 产生脉冲压缩系数 %%%%%%%%%%%%%%%% %--------------------- 正交解调 --------------------% N=tao/ts; n=0:N-1; s1=s(1:N); local_oscillator_i=cos(2*pi*f0/fs*n); % I路本振信号 local_oscillator_q=sin(2*pi*f0/fs*n); % Q路本振信号 fbb_i = local_oscillator_i.*s1; % I路解调 fbb_q = local_oscillator_q.*s1; % Q路解调 window=chebwin(51,40); % 50阶cheby窗的FIR低通滤波器 [b,a]=fir1(50,2*B/fs,window); fbb_i=[fbb_i,zeros(1,25)]; % 因为该FIR滤波器有25个采样周期的延迟，为了保证
第28卷第5期 2004年9月大气科学Chinese Journal of Atmospheric Sciences Vol 128 No 15Sept.2004 2003205208收到,2003210214收到修改稿 3中国科学院百人计划和上海市光科技计划共同资助 基于斐索干涉仪的直接探测多普勒测风激光雷达 3刘继桥 陈卫标 胡企铨 (中国科学院上海光学精密机械研究所先进激光技术与应用系统实验室,上海201800) 摘 要 提出结合多光束斐索(Fizeau )干涉仪和CCD 探测器的条纹图像技术,测量地球边界层下的三维风场的直接探测多普勒激光雷达技术。在分析Fizeau 干涉仪的物理特性和光谱特性以及影响测量多普勒频移的因数和改进方法的基础上,提出一套切合实际的直接探测多普勒激光雷达系统参数。并利用该参数进行性能评估分析,模拟不同干涉仪参数对风速精度的影响,得出一个优化的干涉仪物理参数。模拟结果显示,系统可以获得小于1m s -1的水平风速精度。这些分析,为建立实际的激光雷达系统提供设计依据。 关键词:多光束斐索干涉仪;直接探测;多普勒激光雷达;风速 文章编号 100629895(2004)0520762209 中图分类号 P415 文献标识码 A 1 引言 大气风场是各种天气过程、大气化学成分循环和海气相互作用的主要动力,因此大气风场探测在气象、环境等领域中有着极其重要的地位。多普勒激光雷达已经被认为是精确测量三维风场的有效手段[1]。从全球风场的测量来看,直接探测多普勒激光雷达技术相对相干技术来说存在一定的优势[2]。边缘检测[3]和条纹图像[4]是目前直接探测多普勒激光雷达中最主要的两种多普勒频移测量技术。边缘检测常采用高分辨率的法—伯(FP )干涉仪[3]或者分子[5]、原子吸收线的翼作为鉴频器,其测量灵敏度依赖于分子和气溶胶的后向散射比和风速大小;条纹图像技术则是利用干涉条纹的移动直接测量多普勒频移。Mc G ill 等[6]详细分析、比较了两种测量技术,认为两种技术在风速测量精度十分接近。Mc Kay 等[7]从星载系统的角度比较两种技术,认为条纹图像技术更适合于研制星载激光雷达系统。 最初的条纹图像技术采用FP 干涉仪和图像光电探测器(IPD )得以实现,但这种多阳极光电倍增管的量子效率比较低,而且像元数很有限[8]。Irang 等[9]演示了利用CCD 探测器的条纹图像的直接探测激光雷达,系统利用复杂的二元光学技术将环形条纹转换成点阵[10],增加系统复杂性。因此,相关学者把目光转移到寻找更加适合的干涉仪来代替FP ,如M 2Z 干涉仪[11]和Fizeau 干涉仪[12]。Mc Kay [12]首次分析了利用Fizeau 干涉仪进行多普勒频移检测,其分析是较初步的,也没有针对具体系统进行分析。由于Fizeau 干涉形成的是直线条纹,这样可以利用量子效率较高的线阵固体探测
汽笛声变调的启示--多普勒雷达原理 1842年一天，奥地利数学家多普勒路过铁路交叉处，恰逢一列火车从他身 旁驰过，他发现火车由远而近时汽笛声变响，音调变尖（注：应为“汽笛声的音频频率变高”）；而火车由近而远时汽笛声变弱，音调变低（应为“汽笛声的音频频率降低了”）。他对这种现象感到极大兴趣，并进行了研究。发现这是由于振源与观察者之间存在着相对运动，使观察者听到的声音频率不同于振源频率的缘故，称为频移现象。因为这是多普勒首先提出来的，所以称为多普勒效应。 由于缺少实验设备，多普勒当时没有用实验进行验证。几年后有人请一队小号手在平板车上演奏，再请训练有素的音乐家用耳朵来辨别音调的变化，验证了该效应。 为了理解这一现象，需要考察火车以恒定速度驶近时，汽笛发出的声波在传播过程中表现出的是声波波长缩短，好像波被“压缩”了。因此，在一定时间间隔内传播的波数就增加了，这就是观察者为什么会感受到声调变高的原因；相反，当火车驶向远方时，声波的波长变大，好像波被“拉伸”了。因此，汽笛声听起来就显得低沉。 用科学语言来说，就是在一个物体发出一个信号时，当这个物体和接收者之间有相对运动时，虽然物体发出的信号频率固定不变，但接收者所接收到的信号频率相对于物体发出的信号频率出现了差异。多普勒效应也可以用波在介质中传播的衰减理论解释，波在介质中传播，会出现频散现象，随距离增加,高频向低频移动。 多普勒效应不仅适用于声波,它也适用于所有类型的波，包括电磁波。 多普勒效应被发现以后，直到1930年左右，才开始应用于电磁波领域中。常见的一种应用是医生检查就诊人用的“彩超”，就是利用了声波的多普勒效应。简单地说，“彩超”就是高清晰度的黑白Ｂ超再加上彩色多普勒。超声振荡器产生一种高频的等幅超声信号，向人体心血管器官发射，当超声波束遇到运动的脏器和血管时，便产生多普勒效应，反射信号为换能器所接受，根据反射波与发射波的频率差可以求出血流速度，根据反射波的频率是增大还是减小判定血流方向。 20世纪40年代中期，也就是多普勒发现这种现象之后大约100年，人们才将多普勒效应应用于雷达上。多普勒雷达就是利用多普勒效应进行定位，测速，测距等的雷达。当雷达发射一固定频率的脉冲波对空扫描时，如遇到活动目标，回波的频率与发射波的频率出现频率差（称为多普勒频率），根据多普勒频率的大小，可测出目标对雷达的径向相对运动速度；根据发射脉冲和接收的时间差，可以测出目标的距离。20世纪70年代以来，随着大规模集成电路和数字处理技术的发展，多普勒雷达广泛用于机载预警、导航、导弹制导、卫星跟踪、战场侦察、靶场测量、武器火控和气象探测等方面，成为重要的军事装备以及科学研究、业务应用装置。 多普勒天气雷达，是以多普勒效应为基础，当大气中云雨等目标物相对于雷达发射信号波有运动时，通过测定接收到的回波信号与发射信号之间的频率差异就能够解译出所需的信息。它与过去常规天气雷达仅仅接收云雨目标物对雷达发射电磁波的反射回波进了一大步。这种多普勒天气雷达的工作波长一般为5～10厘米，除了能起到常规天气雷达通过回波测定云雨目标物空间位置、强弱分布、垂直结构等作用，它的重大改进在于利用多普勒效应可以测定降水粒子的运
直接探测多普勒测风激光雷达 引言 风是研究大气动力学和气候变化的一个重要参量，利用风的数据，可以获得大气的变化，并预见其改变，促进人类对能量、水、气溶胶、化学和其它空气物质圈的了解，提高气象分析和预测全球气候变化的能力。目前的风场数据主要来源于无线电探空测风仪、地面站、海洋浮标、观测船、飞行器以及卫星，它们在覆盖范围和观测频率上都存在很大限制。对全球进行直接三维风场测量已经提到日程上来，世界气象组织提出了全球范围的高分辨率大气风场数据的迫切需要，迄今为止，多普勒测风激光雷达是唯一能够获得直接三维风场廓线的工具，具有提供全球所需数据的发展潜力[1]。 激光雷达是探测大气的有力工具，随着激光技术、光学机械加工技术、信号探测、数据采集以及控制技术的发展，激光雷达技术的发展也日新月异。多普勒测风激光雷达具有实用性、高分辨率和三维观测等优点，是其它探测手段难以比拟的[2, 3, 4]。 新研制的1064 nm直接探测多普勒测风激光雷达，利用双边缘技术对对流层三维风场进行探测[5]。本文介绍了该激光雷达 的总体结构及其各部分的功能，并对其探测对流层风场的初步结果进行了分析和讨论。 1 总体结构和技术参数 1064 nm直接探测多普勒测风激光雷达从整体上由激光发射单元、二维扫描单元，回波信号接收单元、信号探测和数据采集单元及控制单元五部分组成，其结构示意图和外观照片分别见图1和图2，主要的技术参数见表1。
激光发射单元、回波信号接收单元、信号探测和数据采集单元放置在光学平台上，保证其光学稳定性。Nd:YAG激光器的中心波长是1064 nm，工作在此波长，可以有较大的激光输出功率，并且气溶胶的后向散射截面比较大。脉冲重复频率为50 Hz，可以节省探测的时间，能捕捉短时间内风速的变化，有利于提高风速探测的准确度。同时，激光器内部注入种子激光可以保证激光器的频率稳定。 二维扫描单元安置在实验房的房顶，接收望远镜的上方。由两个镀有1064 nm 波长全反的介质膜的平面反射镜、水平旋转机构和垂直旋转机构组成的大口径光学潜望式结构。通过软件控制或者手动调节能够全方位扫描，水平方向可以旋转0o至360o，垂直方向可以旋转0o至180o。进行常规探测时采用四波束法，水平方位依次按照0o、90o、180o和270o四个方位探测，即东、南、西和北四个方位，工作仰角为45o。 接收望远镜在二维扫描单元的正下方，有效通光口径为300 mm，如图1所示。主镜镀有1064 nm波长全反的介质膜，反射率高达99%。望远镜接收的大气后向散射回波信号耦合至光纤，由光纤导入到准直镜后成为平行光，经过压制背景光的窄带滤光片后，由20％反射、80％透射的分束片分成两部分。20％的反射信号作为能量探测，由直角反射棱镜分成两束，分别由光子计数探测器接收；80％的透射信号作为信号探测，经过双Fabry-Perot标准具的两个通道后，由于透过率的不一样，得到强度不等的两束光信号，由直角反射棱镜分为两束，由相应的光子计数探测器接收。四个光子计数探测器分别将光信号转换为电信号后，输入光子计数卡内，最后由工控机中的主程序对采集的数据进行储存和处理，并实时显示测量的信号强度廓线、风速和风向。
人教版2019版中考模拟语文试题C卷 姓名:________ 班级:________ 成绩:________ 考试须知： 1、请首先按要求在本卷的指定位置填写您的姓名、班级等信息。 2、请仔细阅读各种题目的回答要求，在指定区域内答题，否则不予评分。 一、选择题 1 . 下列句子没有语病的一项是（） A．在“大众创业、万众创新”的大潮中，凭着青春的激情和对互联网新技术的敏感，使越来越多年轻人加入到“互联网＋”创业的大军中。 B．“一带一路”描绘了一幅和平发展、互利共赢的新思路，它不仅给中国企业带来更多的商机，而且将为世界经济积蓄巨大的力量。 C．为加快我市经济发展的步伐，我们要尽力争取国内外投资，搞好基础设施建设，发展生态节水农业、文化旅游业和服务业。 D．纪录片《舌尖上的中国》不仅引发了人们对“文化认同”和“软实力输出”的思考，而且让人怀念童年时的美味。 2 . 下列说法正确的一项是（） A．《最苦与最乐》选自《梁启超文选》，作者梁启超是明代思想家、学者。 B．《邹忌讽齐王纳谏》选自《战国策》，《战国策》是战国时游说之士的策谋和言论的汇编，由东汉的刘向编订为三十三篇。 C．词又称“长短句”，句式长短不一。兴盛于宋代，苏轼和辛弃疾是豪放派的代表人物，而李清照是婉约派的代表人物。 D．《岳阳楼记》实际上是一篇借物言志的文章，寄寓了作者与民同乐的思想。 3 . 下列加点词语注音完全正确的一项（） A．畸形(qí)诘责(jié)文绉绉(zhōu)眼翳(yì) B．胡髭(zī)黝黑(yǒu)一绺绺(liǔ )颔首(hàn) C．直戳(chuō)解剖(pāo)诱惑(yòu)摹画(mó)
脉冲多普勒雷达的总结 1、适用范围 脉冲多普勒（PD）雷达是在动目标显示雷达基础上发展起来的一种新型雷达体制。这种雷达具有脉冲雷达的距离分辨力和连续波雷达的速度分辨力，有更强的抑制杂波的能力，因而能在较强的杂波背景中分辨出动目标回波。 2、PD雷达的定义及其特征 （1）定义：PD雷达是一种利用多普勒效应检测目标信息的脉冲雷达。 （2）特征：①具有足够高的脉冲重复频率（简称PRF），以致不论杂波或所观测到的目标都没有速度模糊。 ②能实现对脉冲串频谱单根谱线的多普勒滤波，即频域滤波。 ③PRF很高，通常对所观测的目标产生距离模糊。 3、PD雷达的分类 图1 PD雷达的分类图 ①MTI雷达（低PRF）：测距清晰，测速模糊 ②PD雷达（中PRF）：测距模糊，测速模糊，是机载雷达的最佳波形选择 ③PD雷达（高PRF）：测距模糊，测速清晰 4、机载下视PD雷达的杂波谱分析 机载下视PD雷达的地面杂波是由主瓣杂波、旁瓣杂波和高度线杂波所组成的。 、PRF 的选择 （1）高、中、低脉冲重复频率的选择 ①机载雷达在没有地杂波背景干扰的仰视情况下，通常采用低PRF加脉冲压缩。 ②迎面攻击时高PRF优于中PRF。尾随时，在低空，中PRF优于高PRF ;在高空，高PRF优于中PRF。 ③交替使用中、高PRF的方法，或者再加上在下视时采用低PRF的方法，并在低、中PRF时配合采用脉冲压缩技术，将是在所有工作条件下得到远距离探测性能的最有效的方
法。 （2）高PRF时重复频率的选择 ①使迎面目标谱线不落人旁瓣杂波区中: ②为了识别迎面和离去的目标： A、当接收机单边带滤波器对主瓣杂波频率固定时： B、当接收机单边带滤波器相对发射频率是固定时： 注：单边带滤波器的通带范围应从，单边带滤波器的中心频率是固定的，但偏离应为。 6、PD雷达的信号处理系统 PD雷达的信号处理系统主要由单边带滤波器、主瓣杂波抑制滤波器、零多普勒频率抑制滤波器、多普勒滤波器组、检波积累、转换器和门限等部分组成，下面总结各组成部分的特点及其实现方法。 （1）单边带滤波器 特点：带宽近似等于脉冲重复频率fr, 一般设置在中频； 从回波频谱中只滤出单根谱线； 避免了后面信号处理过程中可能产生的频谱折叠效应; 距离选通波门必须设在单边带滤波器之前; 要求带外抑制至少要大于60dB; 实现方法：采用石英晶体滤波器 （2）主瓣杂波抑制滤波器 特点：比目标回波能量要高出60-80dB； 主瓣杂波抑制滤波器的幅一频特性应是主瓣杂波频谱包络的倒数； 相当于一个白化滤波器，经过主瓣杂波抑制之后，后面的多普勒滤波器可以 按照白噪声中的匹配滤波理论来进行设计； 实现方法：首先确定它的频率，用一个混频器先消除变化的，就可以用一个固定频率的滤波器将其滤除. 确定主瓣杂波中心频率有两种方法:一种方法是利用频率跟踪； 另一种是由天线指向和载机飞行速度计算出主瓣杂波应有的多普勒频移，直接控制压 控振荡器去产生的振荡濒率。 （3）零多普勒频率抑制滤波器 特点：用于高度杂波的滤除； 同时抑制发射机直接进人到接收机的泄漏； 实现方法：①只需断开滤波器组中落人高度杂波区的那些子滤波器的输出； ②使用可防止检测高度线杂波专用的CFAR电路; ③使用航迹消隐器除去最后输出的高度线杂波。 （4）多普勒滤波器组 特点：是覆盖预期的目标多普勒频移范围的一组邻接的窄带滤波器； 起到了实现速度分辨和精确测量的作用； 可以设在中频，也可以设在视频；