CN109617839A - A kind of Morse signal detection method based on Kalman filtering algorithm - Google Patents
A kind of Morse signal detection method based on Kalman filtering algorithm Download PDFInfo
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Abstract
The Morse signal detection method based on Kalman filtering algorithm that the invention discloses a kind of, by carrying out bit synchronization to signal using Self-synchronous algorithm, adaptive energy threshold value is set for interference noise energy time-varying using Kalman filtering algorithm, soft-decision is carried out with the actual energy value to the signal being calculated, to identify pure CW signal.The method provided through the invention can effectively synchronize under strong noise background and detect CW signal, and strong antijamming capability, the bit error rate is lower, and adaptivity is strong, and real-time is more preferable.
Description
Technical field
The present invention relates to communication signal processing technology fields, are based on Kalman filtering algorithm more specifically to one kind
Morse signal detection method.
Background technique
High frequency CW (Morse) signal communication is a kind of radio communication system in 3-30MHz frequency range, required equipment
Simply, band occupancy is narrow, emission effciency is high, is that a kind of signal resolution is very high, and anti-interference ability is very strong, equal-wattage and communicate
A kind of farthest communication mode of distance, and its emitter is simple, can work in the environment of low signal-to-noise ratio, is that current tactics are logical
The important means of letter, while having very extensively in the flexibilities such as navigation, aviation communication and anti-interference more demanding civilian aspect
General application.High frequency CW signal communication technology is generally divided into automatic signal detection technology and two steps of click and sweep identification technology.CW
The purpose of automatic signal detection is that Detection and Extraction go out pure CW time domain plethysmographic signal under noise background, inhibit ambient noise and
Influence of the interchannel noise to CW signal；And click and sweep identification is identified from the pure CW signal time sequence that Detection and Extraction go out
The click and sweep sequence of Morse code is translated into corresponding character further according to the decoding rule of Morse code.
CW communication for a long time is all that manually operation is completed, but shortwave system on HF ionospheric channels declines there are serious
Phenomenon and multipath effect are fallen, while considering that war communications electromagnetic environment is very severe, it is higher in very noisy interference and bit rate
In the case of, human ear is difficult to distinguish information represented by CW signal, and the development of modern communication technology, communication process gradually add
Fastly, artificial CW communication speed is slow, and the bit error rate is relatively high, so that communication performance is caused to decline,
It is many to the research of CW automatic signal detection decoding at present, but inspection can be synchronized under strong noise background not yet
Survey the method for extracting pure high frequency CW signal." Morse code automatic decode system " (be published in June, 2007 " war industry from
Dynamicization ") in the extraction of CW signal is carried out using envelope detection algorithm, for such method, when bit rate is higher or very noisy is dry
The variation that will be difficult to real-time tracking signal waveform is disturbed down, so cause the bit error rate high, performance degradation.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of Morse signal detection side based on Kalman filtering algorithm
Method, in this way, can effectively synchronize under strong noise background and detect CW signal, the bit error rate is lower, better performances.
To achieve the above object, specific technical solution of the present invention is as follows:
A kind of Morse signal detection method based on Kalman filtering algorithm, comprising:
S101: Domain Synchronous positioning is carried out to the signal received according to self-synchronizing method, and the signal after synchronizing is carried out
Segmentation；
S102: calculating actual energy value of first block signal in time domain on preset characteristic frequency point, described
It include priori header information in first block signal；
S103: state-noise variance and observation noise variance are determined according to the priori header information；
S104: described first is determined based on Kalman filtering algorithm and the state-noise variance and observation noise variance
The corresponding energy threshold of a block signal；
S105: when the actual energy value is less than the energy threshold, determine that first block signal is More
This signal, otherwise, it determines first block signal is noise signal；
S106: actual energy value of next block signal on preset characteristic frequency point is calculated；
S107: next segmentation is determined based on Kalman filtering algorithm and the state-noise variance and observation noise variance
The corresponding energy threshold of signal；
S108: it in the actual energy value of next block signal energy threshold corresponding less than next block signal, determines
Next block signal is Morse signal, otherwise, it determines next block signal is noise signal；
S109: judge whether to finish block signal detection, if so, going to S110, otherwise, go to S106；
S110: terminate detection.
Further, described to include: to the signal progress Domain Synchronous positioning received according to self-synchronizing method
Down coversion is carried out to the signal received and obtains baseband signal；
Secondary down coversion is carried out to the baseband signal, and is obtained according to secondary down coversion result and point pulse duration
To synchronous sinusoidal signal theoretic frequency；
Spike detection is carried out in base-band signal spectrum according to the theoretic frequency and extracts synchronous sinusoidal signal；
Domain Synchronous positioning is carried out to the signal received according to the synchronous sinusoidal signal.
Further, calculating actual energy value of the block signal on preset characteristic frequency point includes:
Block signal is calculated in the actual energy value of preset characteristic frequency point based on Goerzel algorithm.
Further, described that block signal is calculated in the actual energy value of preset characteristic frequency point based on Goerzel algorithm
Include:
For a block signal, after completing to the sampling of the block signal, segmentation letter is calculated in the following way
Number preset characteristic frequency point actual energy value X (k):
Wherein, vk(N) the corresponding energy value of n-th sampled point, v are indicatedk(N-1) the corresponding energy of the N-1 sampled point is indicated
Magnitude, N are sampled point number,fsFor sample frequency, f0For the frequency values of preset characteristic frequency point, k indicates kth
A point dot corresponding position time point.
Further, segmentation is determined based on Kalman filtering algorithm and the state-noise variance and observation noise variance
The corresponding energy threshold of signal includes:
For a block signal, when completing to receive the block signal, calculating in the following way should under current time
Energy threshold corresponding to block signal:
Z (k | k)=Z (k/k-1)+K (k) ε (k), P (k | k)=[I-K (k) H] P (k/k-1), wherein Z (k | k-1)=φ
Z (k-1/k-1), K (k)=P (k/k-1) HT[HP(k/k-1)HT+R]-1, P (k | k-1)=φ P (k-1/k-1)+Γ Q ΓT, ε (k)
=Y (k)-HZ (k/k-1), Y (k)=ratio × max [X (k), X (k-1) ... X (k-7)], ratio=0.6, φ are to set in advance
The state-transition matrix set, H are pre-set observing matrix, and Γ is that pre-set noise drives matrix, and Q is according to
Priori header information determine state-noise variance, R be according to the priori header information determine observation noise variance, Z (k |
It k) is the energy threshold at k-th of point dot corresponding position time point, and P (k | k) it is k-th of point dot corresponding position time point
Covariance, the energy threshold initial value Z (0 | 0) and covariance initial value P (0 | 0) of each block signal are according to the block signal
Energy value corresponding to first sampled point obtains.
Further, the energy threshold initial value Z (0 | 0) of each block signal is according to Z (0 | 0)=E [x (0)]=u0Meter
It obtains, E [x (0)] is to adopt to first of the block signal before being in the block signal in the block signal and timing
Energy value corresponding to sampling point is averaged, and the covariance initial value P of each block signal (0 | 0) according to E [(x (0)-u0)(x
(0)-u0)T] be calculated.
A kind of Morse signal detection method based on Kalman filtering algorithm provided by the invention, utilizes Self-synchronous algorithm
Bit synchronization is carried out to signal, adaptive energy threshold value is set for interference noise energy time-varying using Kalman filtering algorithm, with
Soft-decision is carried out to the actual energy for the signal being calculated, to identify pure CW signal.It provides through the invention
Method can effectively synchronize under strong noise background and detect CW signal, and adaptivity is strong, and the bit error rate is lower, anti-interference energy
Power is strong, and real-time is more preferable.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is that the process of the Morse signal detection method provided in an embodiment of the present invention based on Kalman filtering algorithm is shown
It is intended to；
Fig. 2 is the flow diagram provided in an embodiment of the present invention synchronized to the signal received；
Fig. 3-1 is the spectrogram of baseband signal provided in an embodiment of the present invention；
Fig. 3-2 is the spectrogram after secondary down coversion provided in an embodiment of the present invention；
Fig. 3-3 is the schematic diagram of spike detection provided in an embodiment of the present invention；
Fig. 3-4 synchronizes the waveform diagram after positioning to signal to be provided in an embodiment of the present invention.
Specific embodiment
In order to keep the technical problem to be solved in the present invention, technical solution and advantage clearer, below in conjunction with attached drawing and
Specific embodiment is described in detail, it should be understood that the specific embodiments described herein are merely illustrative of the present invention, not
For limiting the present invention.
The Morse signal detection method based on Kalman filtering algorithm that the present embodiment provides a kind of, it is shown in Figure 1,
Include:
S101: Domain Synchronous positioning is carried out to the signal received according to self-synchronizing method, and the signal after synchronizing is carried out
Segmentation.
It is all not synchronized to signal when needing to detect CW signal in existing communication system
On the basis of directly CW signal is handled, due to short-wave ionospheric radio channel, it is serious to accumulate multipath phenomenon, not only causes
The decline of signal amplitude, and cause interference effect, so that signal is generated distortion and multidiameter delay, to solve this problem, this reality
It applies in example and the signal received is synchronized first before being detected to signal.
Any one communication system is all the combination for sending signal and receiving signal, and the premise for receiving signal seeks to realize
The synchronization of system obtains complete code element information to obtain the start/stop time of symbol signal, so the quality of net synchronization capability is directly
There is synchronous error or loses to synchronize to will lead to communication system performance decline or communication disruption in the performance for influencing communication system.This
In embodiment, receiver can execute step shown in Fig. 2 after receiving signal to realize the synchronization to signal:
S21: down coversion is carried out to the signal received and obtains baseband signal.
It include the synchronizing information of CW electric signal in baseband signal.The spectrogram of baseband signal may refer to figure in the present embodiment
Shown in 3-1.
S22: secondary down coversion is carried out to baseband signal, and is obtained according to secondary down coversion result and point pulse duration
To synchronous sinusoidal signal theoretic frequency.
The point pulse duration can obtain according to communications codes rate calculations.Spectrogram after secondary down coversion may refer to
Shown in Fig. 3-2.
S23: spike detection is carried out in base-band signal spectrum according to theoretic frequency and extracts synchronous sinusoidal signal.
There is the how general influence extended with Doppler frequency shift in propagating in practical system on HF ionospheric channels, it is possible to which setting is suitable
When frequency range spike detection is carried out in base-band signal spectrum, the schematic diagram of spike detection may refer to shown in Fig. 3-3.
S24: Domain Synchronous positioning is carried out to the signal received according to synchronous sinusoidal signal.
The schematic diagram of synchronous positioning may refer to shown in Fig. 3-4.
S102: actual energy value of first block signal on preset characteristic frequency point in calculating time domain, first
It include priori header information in a block signal.
Preset characteristic frequency point can be arbitrarily arranged in the present embodiment, it is preferred that should be between 300Hz to 3400Hz
Any one Frequency point, for example can be set to 1000Hz.Ge Zeer (Goertzel) algorithm can be based in the present embodiment
Each block signal is calculated in the actual energy value of preset characteristic frequency point.It should be understood that can also use existing
Other algorithms calculate energy value.
Goertzel algorithm is a kind of algorithm improved on the basis of discrete fourier transform, and x (n) is for segmentation
The sample sequence that signal is sampled, the discrete Fourier transform (DFT) of sequence x (n)
Are as follows:
In formula: WN=e-j2π/N。
This first order recursive system algorithm is improved to a second-order system recursive algorithm, transfer function are as follows:
Above formula second-order system, which can be used to lower difference equation, to be indicated:
Wherein, N is sampled point number,fsFor sample frequency, f0For the frequency values of preset characteristic frequency point,
So X (k) can be iterated to calculate by Goertzel algorithm to obtain reality of the block signal on preset characteristic frequency point
Energy value.
Actual energy value of the block signal on preset characteristic frequency point is X (k).
Goertzel algorithm can obtain frequency real and imaginary parts identical with conventional discrete Fou-rier transformation DFT or FFT, but
It is that Goertzel algorithm can obtain signal in the spectral magnitude size of specific frequency, does not need the spectrum value for calculating entire frequency band,
It can be handled immediately after each sample simultaneously, Processing Algorithm algorithm is carried out to blocking sampling compared to FFT, is used
Goertzel algorithm is more efficient, and operand is small, real-time is stronger.
S103: state-noise variance and observation noise variance are determined according to priori header information.
According to the actual energy value that Goertzel algorithm obtains, there are two types of types: the energy of useful signal energy value and noise
The sum of value, in the various intervals of CW signal (including point ' dot ' and stroke ' dash ' are spaced, are spaced between interval, word between character)
The energy value of interchannel noise and background interference noise decides whether so a kind of threshold value is arranged as useful signal energy, but
It is that ionospheric channel is random channel in high-frequency communication, fixed judgment threshold cannot cope with interference noise intensity time-varying problem,
So that the recognition result bit error rate increases.Kalman (Kalman) filtering algorithm can be from a series of measurements for completely including noise
In, estimate the optimum state of a dynamical system, especially track Dynamic Signal under strong noise background, estimates in dynamic system optimal
There is good performance in meter, and recursion iteration can be can be carried out to dbjective state and realize optimal estimation.But the standard of CW signal
Kalman filter requires the statistical property of known system noise, the noise of the system model of mistake, measurement model or inaccuracy
Statistical value will lead to estimated value and generate Divergent Phenomenon, so to synchronize on the basis of priori header information in the present embodiment
Detection identification, to obtain the statistical property of communication channel noise signal.By Kalman Filter Technology, adaptive threshold pair is set
Energy value X (k) makes decisions identification, and adaptive threshold can be adjusted dynamically to cope with the time-varying of interference noise energy.Kalman
Filtering uses the state-space model of signal and noise, updates shape using the estimated value and the observation of current moment of previous moment
The estimated value of the present moment of state variable.
Therefore, transmitter is needed when sending signal to receiver plus priori header before effective message, in this way, connecing
Receipts machine can determine state-noise variance and observation noise variance according to the priori header information received.
S104: first segmentation letter is determined based on Kalman filtering algorithm and state-noise variance and observation noise variance
Number corresponding energy threshold.
When completing to receive the block signal can be calculated in the following way current time for a block signal
Energy threshold corresponding to the lower block signal:
Z (k | k)=Z (k/k-1)+K (k) ε (k), P (k | k)=[I-K (k) H] P (k/k-1),
Z (k | k-1)=φ Z (k-1/k-1), K (k)=P (k/k-1) HT[HP(k/k-1)HT+R]-1,
P (k | k-1)=φ P (k-1/k-1)+Γ Q ΓT, ε (k)=Y (k)-HZ (k/k-1),
Y (k)=ratio × max [X (k), X (k-1) ... X (k-7)], ratio=0.6,
φ is pre-set state-transition matrix, and H is pre-set observing matrix, and Γ is the drive of pre-set noise
Dynamic matrix, it is all unit matrix that state, which shifts square φ, observing matrix H, noise driving matrix Γ, and state-noise and observation noise are equal
Error for zero mean Gaussian white noise process, front and back moment is irrelevant, corresponding statistical property variance Q and R, according to Q
The state-noise variance that priori header information determines, R are the observation noise variance determined according to priori header information, and Z (k | k) is
The energy threshold at k-th of point dot corresponding position time point, P (k | k) are the association side at k-th of point dot corresponding position time point
The current corresponding covariance of difference and the block signal, the energy threshold initial value Z (0 | 0) and covariance of each block signal
Initial value P (0 | 0) energy value can be calculated corresponding to first sampled point based on the block signal.It should manage
Solution, above-mentioned preset matrix can be configured based on existing mode, and which is not described herein again.
Z (k | k-1) in above-mentioned formula is energy threshold predicted value, K (k) is Kalman filtering gain, P (k | k-1) is
Error covariance matrix predicted value, Y (k) they are observation, specifically, Z (k | k-1), K (k), P (k | k-1), Y (k) and ε (k)
It can be understood as being the intermediate variable for calculating Z (k | k) and P (k | k).
It should be noted that for each block signal, it can be referring to this when calculating its corresponding energy threshold
In method calculated.
The energy threshold initial value Z (0 | 0) of each block signal is according to Z (0 | 0)=E [x (0)]=u0It is calculated, E [x
(0)] for corresponding to first sampled point to the block signal in the block signal and timing before the block signal
Energy value average, the covariance initial value P of each block signal (0 | 0) is according to E [(x (0)-u0)(x(0)-u0)T] calculate
It obtains.
S105: when the actual energy value of first block signal is less than its corresponding energy threshold, the first point is determined
Segment signal is Morse signal, otherwise, it determines first block signal is noise signal.
S106: actual energy value of next block signal on preset characteristic frequency point is calculated.
It should be noted that it must execute after step S105 that the step S106 in the present embodiment, which is not offered as it,
In practice, it will be detected immediately after receiving the block signal, its corresponding actual energy value is calculated, to sentence
Breaking, it is CW signal or noise signal, so there is no inevitable sequencings by step S106 and step S103-S105.
S107: next block signal is determined based on Kalman filtering algorithm and state-noise variance and observation noise variance
Corresponding energy threshold.
S108: it in the actual energy value of next block signal energy threshold corresponding less than next block signal, determines
Next block signal is Morse signal, otherwise, it determines next block signal is noise signal.
S109: judge whether to finish block signal detection, if so, going to S110, otherwise, go to S106；
S110: terminate detection.
It should be noted that can be carried out using existing recognizer to CW signal after Detection and Extraction go out CW signal
Click and sweep identification, is translated into corresponding character.
Not strong for the noise robustness of current high frequency CW signal detecting method, the problem of real-time difference, the present embodiment mentions
A kind of CW signal detecting method based on Kalman filtering out obtains the statistics of communication channel noise by handling header signal
Characteristic is then based on Kalman filtering iteration recursion and goes out optimal adaptive energy threshold value, eliminates channel multipath effect and interference
Influence of the noise to signal extracts pure CW signal and carries out identification decoding.In the interference of strong white Gaussian noise and practical shortwave
Under communication environment detect recognition performance it is good, adaptivity and strong real-time, can recursion realize, the automatic knowledge to high frequency CW signal
The anti-interference ability that Jian Ce and not improve high frequency CW signal has important practical value.
It is noted that herein, the terms "include", "comprise" or its any other variant are intended to non-exclusive
Property include so that include a series of elements process, method, article or device not only include those elements, but also
Further include other elements that are not explicitly listed, or further include for this process, method, article or device it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including this
There is also other identical elements in the process, method of element, article or device.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal (can be mobile phone, computer, service
Device, air conditioner or network equipment etc.) execute method described in each embodiment of the present invention.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (6)
1. a kind of Morse signal detection method based on Kalman filtering algorithm characterized by comprising
S101: Domain Synchronous positioning is carried out to the signal received according to self-synchronizing method, and the signal after synchronizing is segmented；
S102: actual energy value of first block signal on preset characteristic frequency point in calculating time domain, described first
It include priori header information in a block signal；
S103: state-noise variance and observation noise variance are determined according to the priori header information；
S104: described first point is determined based on Kalman filtering algorithm and the state-noise variance and observation noise variance
The corresponding energy threshold of segment signal；
S105: when the actual energy value is less than the energy threshold, determine first block signal for Morse's letter
Number, otherwise, it determines first block signal is noise signal；
S106: actual energy value of next block signal on preset characteristic frequency point is calculated；
S107: next block signal is determined based on Kalman filtering algorithm and the state-noise variance and observation noise variance
Corresponding energy threshold；
S108: it in the actual energy value of next block signal energy threshold corresponding less than next block signal, determines next
Block signal is Morse signal, otherwise, it determines next block signal is noise signal；
S109: judge whether to finish block signal detection, if so, going to S110, otherwise, go to S106；
S110: terminate detection.
2. the Morse signal detection method based on Kalman filtering algorithm as described in claim 1, which is characterized in that described
Carrying out Domain Synchronous positioning to the signal received according to self-synchronizing method includes:
Down coversion is carried out to the signal received and obtains baseband signal；
Secondary down coversion is carried out to the baseband signal, and is obtained together according to secondary down coversion result and point pulse duration
Walk sinusoidal signal theoretic frequency；
Spike detection is carried out in base-band signal spectrum according to the theoretic frequency and extracts synchronous sinusoidal signal；
Domain Synchronous positioning is carried out to the signal received according to the synchronous sinusoidal signal.
3. the Morse signal detection method based on Kalman filtering algorithm as described in claim 1, which is characterized in that calculate
Actual energy value of the block signal on preset characteristic frequency point include:
Block signal is calculated in the actual energy value of preset characteristic frequency point based on Goerzel algorithm.
4. the Morse signal detection method based on Kalman filtering algorithm as claimed in claim 3, which is characterized in that described
Calculating block signal in the actual energy value of preset characteristic frequency point based on Goerzel algorithm includes:
Is calculated by the block signal in the following way and is existed after completing to the sampling of the block signal for one block signal
The actual energy value X (k) of preset characteristic frequency point:
Wherein, vk(N) the corresponding energy value of n-th sampled point, v are indicatedk(N-1) the corresponding energy of the N-1 sampled point is indicated
Value, N are sampled point number,fsFor sample frequency, f0For the frequency values of preset characteristic frequency point, k is indicated k-th
Point dot corresponding position time point.
5. the Morse signal detection method based on Kalman filtering algorithm as claimed in claim 4, which is characterized in that be based on
Kalman filtering algorithm and the state-noise variance and observation noise variance determine the corresponding energy threshold packet of block signal
It includes:
For a block signal, when completing to receive the block signal, the segmentation under current time is calculated in the following way
Energy threshold corresponding to signal:
Z (k | k)=Z (k/k-1)+K (k) ε (k), P (k | k)=[I-K (k) H] P (k/k-1), wherein, Z (k | k-1)=φ Z (k-
1/k-1), K (k)=P (k/k-1) HT[HP(k/k-1)HT+R]-1, P (k | k-1)=φ P (k-1/k-1)+Γ Q ΓT, ε (k)=Y
(k)-HZ (k/k-1), Y (k)=ratio × max [X (k), X (k-1) ... X (k-7)], ratio=0.6, φ are to preset
State-transition matrix, H be pre-set observing matrix, Γ be pre-set noise drive matrix, Q be according to the elder generation
The state-noise variance that header information determines is tested, R is the observation noise variance determined according to the priori header information, Z (k | k)
For the energy threshold at k-th of point dot corresponding position time point, P (k | k) is the association at k-th of point dot corresponding position time point
Variance, the energy threshold initial value Z (0 | 0) and covariance initial value P (0 | 0) of each block signal are according to the of the block signal
Energy value corresponding to one sampled point obtains.
6. the Morse signal detection method based on Kalman filtering algorithm as claimed in claim 5, which is characterized in that each
The energy threshold initial value Z (0 | 0) of block signal is according to Z (0 | 0)=E [x (0)]=u0It is calculated, E [x (0)] is to this point
Energy value corresponding to first sampled point of the block signal on segment signal and timing before the block signal asks equal
Value, the covariance initial value P (0 | 0) of each block signal is according to E [(x (0)-u0)(x(0)-u0)T] be calculated.
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| CN113177514A (en) * | 2021-05-20 | 2021-07-27 | 浙江波誓盾科技有限公司 | Unmanned aerial vehicle signal detection method and device and computer readable storage medium |
| CN114184848A (en) * | 2021-12-03 | 2022-03-15 | 中国科学院国家空间科学中心 | Goertzel algorithm-based point-by-point scanning real-time processing method for satellite-borne VHF transient signals |
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