CN105812300A - Long code DSSS signal blind estimation method for eliminating information code hopping - Google Patents

Long code DSSS signal blind estimation method for eliminating information code hopping Download PDF

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CN105812300A
CN105812300A CN201610292502.5A CN201610292502A CN105812300A CN 105812300 A CN105812300 A CN 105812300A CN 201610292502 A CN201610292502 A CN 201610292502A CN 105812300 A CN105812300 A CN 105812300A
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hopping
signal
information code
information
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CN105812300B (en
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夏文龙
郭庆功
周渊平
马雪莲
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Sichuan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0238—Channel estimation using blind estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/69—Spread spectrum techniques
    • H04B1/707—Spread spectrum techniques using direct sequence modulation

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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  • Optical Communication System (AREA)
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Abstract

The invention discloses a long code direct sequence spread spectrum (DSSS) signal blind estimation method for eliminating information code hopping, and relates to the field of spread-spectrum signal processing during signal processing. Technical key points of the long code DSSS signal blind estimation method are as follows: in consideration that a long code is interfered by multiple different information code elements during one period, an interference reason is: random time varying of an information code causes existence of hopping between a front code element and a next code element, and a hopping point varies the polarity of a spread-spectrum code, so that long code spread spectrum signal estimation generates errors; and according to the long code DSSS signal blind estimation method for eliminating the information code hopping, a hopping point compensation matrix is constructed to a receiving signal, and influence of a hopping point is eliminated, so that a long code spread spectrum sequence is estimated by directly utilizing a principal component decomposition method under an condition of not needing of segmentation. The long code DSSS signal blind estimation method for eliminating the information code hopping is especially suitable for performing blind detection and blind estimation of long code spread spectrum signals in non-cooperative communication, and processing and detecting software radio signals of a spread spectrum system.

Description

Long code DSSS signal blind estimation method for eliminating information code hopping
Technical Field
The invention relates to the field of spread spectrum signal processing in signal processing, in particular to a long code DSSS signal blind estimation method for eliminating information code hopping.
Background
The Direct Sequence Spread Spectrum (DSSS) modulation technique uses a high-rate pseudorandom sequence to spread the information signal, and features a spread spectrum in the frequency domain, and the direct sequence spread spectrum technique has the characteristics of confidentiality, multipath resistance, interference resistance and interception resistance, and is widely applied to civil and military communications. In a cooperative communication scenario, a receiving end despreads signals by using parameters known to both the transmitting and receiving ends to identify transmitted information. In non-cooperative communication, such as radio detection, positioning, information recovery, etc., blind processing of the direct sequence spread spectrum signal is required, wherein the most important parameter is the pseudorandom sequence used for acquiring the spread spectrum.
Direct sequence spread spectrum signals are generally classified into short code direct spread spectrum signals and long code direct spread spectrum signals according to the number of information symbols modulated by one spreading code period (pseudo random sequence period). For a short code direct sequence spread spectrum signal, the information code duration is just equal to one spreading code period, namely one spreading period modulates one information code element; for a long code direct sequence spread spectrum signal, the information code duration is less than the spreading code period, i.e., one spreading period modulates K (K is a positive rational number greater than 1, and may be an integer or a fraction, and corresponds to a periodic long code and a non-periodic long code, respectively) information codes. The long code direct sequence spread spectrum signal is more difficult to estimate the shorter code due to the interference of the information code modulation. Chinese patent (publication No. CN101237250A, published 2008/8/6) discloses a blind estimation method of spreading waveform based on singular value analysis, which segments the received spreading signal, estimates the spreading code waveform in each segment of data, and connects the spreading code waveforms to obtain a spreading sequence of one period. The method needs to divide the spread spectrum code into segments smaller than the spread spectrum period, the selection of the segmentation parameters can influence the estimation performance, and the syndrome decomposition operation needs to be carried out for a plurality of times, so that the calculation complexity is increased. Chinese patent (publication No. CN103414670A, published 2013, 11/27) discloses a long code DSSS signal blind despreading method based on semi-definite programming, which estimates a spreading code waveform by constructing a symmetric matrix and using an interior point algorithm. The method has higher calculation complexity and longer time consumption than the first method.
Disclosure of Invention
The invention aims to solve the technical problem of providing a long code DSSS signal blind estimation method for eliminating information code hopping, and solving the problem of complex operation in the prior art.
The technical scheme provided by the invention for solving the problems is that the long code DSSS signal blind estimation method for eliminating information code hopping comprises the following steps:
a. obtaining a baseband sampling signal by down-converting an air interface signal, obtaining a long code spread spectrum DSSS signal period P, a chip rate and an information code element time width Q by blind estimation, and intercepting a digital signal with the length of N segments being P from the sampling signal to obtain the following expression
y ( n ) = A Σ i = 0 M - 1 b i q ( n - j Q ) Σ j = 0 N - 1 h ( n - j P ) + w ( n )
h ( n ) = Σ k = 0 P - 1 c k p ( n - k )
Wherein b isi(bi∈ { ± 1}, i ═ 0., M-1) and ck(ck∈ { ± 1}, k 0.., P-1) represents an information code sequence and a spreading code sequence, respectively, a represents the amplitude of a signal, M represents the number of information codes in NP signal samples, and q (n) is a rectangular function and satisfiesp (n) denotes the convolution of the transmit filter, channel and receive filter, h (n) is the spreading code sequence ckAnd an impulse response of p (n), w (n) representing additive white gaussian noise;
b. calculating the number of information code elements contained in a spreading code period according to the estimated time width Q of the information code elements and the spreading code period P The mathematical sign represents rounding up, so that in a long code spreading period, the information code element has β jump points at most to construct a jump mode matrix
Wherein Hk(k 0.., β) represents k jumpsA hopping pattern matrix corresponding to the variable points;
c. segmenting the received baseband signal with the long code with noise according to the period P to obtain N vectors y with the length of Pl(l ═ 0.., N-1), where
Wherein,representing a sequence of information symbols sampled at a chip rate;
d. computingIn thatMust have a group of hopping patterns and blThe hopping patterns of the medium information codes are the same, i.e. Is a matrix of × P, thenCan be expressed as
s.t.||bl(1：P)||2＝P
| b l ( i ) | = 1 , ∀ i ∈ { 0 , 1 , ... P - 1 }
e. According toComputing a covariance matrixTo pairPerforming principal component decomposition to obtain corresponding feature vector wmaxThereby obtaining an estimated spreading code sequence
c ^ = s i g n ( w max )
Where sign (·) represents a sign function.
Considering that a long code is interfered by a plurality of different information code elements in a period, the reason of the interference is that jump exists between the front code element and the rear code element due to the randomness of the information code, and a jump point changes the polarity of a spread spectrum code, so that the estimation of a long code spread spectrum signal brings errors.
Further, in the step a, the blind estimation of the long code spread spectrum DSSS signal period P estimation adopts a quadratic spectrum method, a correlation fluctuation spectrum method, a cepstrum method, a cyclic spectrum method or an autocorrelation difference method.
Further, in step b, a submatrix H in the hopping pattern matrixkA jump position is denoted by 1 to-1 or-1 to 1. k is 0 to indicate that no jumping point exists, corresponding to all 1 row vectors with the length of P, k is 1 to represent that only one jumping point exists, the jumping position can occur at the position between any front and back adjacent chips in P chips at the time of one jumping point, P-1 row vectors exist in the worst case, and the like, thereby determining the whole information code jumping matrix
Further, in step b, the hopping pattern matrix can also be approximately represented by a matrix formed by the hopping patterns of the hopping points with the maximum hopping probability, that is, the information code hopping matrix can be usedTo reduce the dimensionality of the matrix.
Further, in step d, the operatorRepresentation matrixThe elements in each row in (1) and ylThe corresponding elements in the vector are dot multiplied.
Further, in step e, the principal component decomposition is realized by singular value decomposition or eigenvalue decomposition.
The invention has the advantages that the damage of the information code to the spread spectrum code sequence period in the long code spread spectrum is eliminated by constructing the information code hopping mode matrix, the blind estimation of the long code spread spectrum sequence is realized by the principal component analysis method, the better estimation performance is realized with lower complexity, and the performance is greatly improved compared with the prior method especially under the condition of low signal to noise ratio. The invention is particularly applicable to blind detection and blind estimation of long code spread spectrum signals in uncooperative communications, and software radio signal processing and detection for spread spectrum systems.
The present invention is further described below in conjunction with the appended drawings to enable those skilled in the art to practice the invention.
Drawings
FIG. 1 is a flow chart of a method for blind estimation of a long code DSSS signal to eliminate information code hopping;
FIG. 2 is a graph comparing the performance of the present invention and a conventional eigenvalue decomposition as a function of the sampling period length;
FIG. 3 is a graph of a comparison of spreading code de-spreading correlation coefficients as a function of signal-to-noise ratio for the present invention and a conventional eigenvalue decomposition;
FIG. 4 is a graph of the bit error rate of an information code as a function of signal-to-noise ratio for the present invention and for conventional eigenvalue decomposition and cooperative despreading;
Detailed Description
As shown in fig. 1, a flowchart of a method for blind estimation of a long code DSSS signal without information code hopping is shown, and the method for blind estimation of a long code DSSS signal without information code hopping includes the following steps:
step 100: acquiring a baseband sampling signal from an air interface;
step 101: and blind estimation is carried out to obtain the period P of the long code spread spectrum DSSS signal, the chip rate and the time width Q of the information code element. The estimation of P and Q can be realized by adopting a secondary power spectrum, a related fluctuation spectrum, a cepstrum, a cyclic spectrum or an autocorrelation difference method, and then N digital signals with the length of P are intercepted from the sampling signals
y ( n ) = A Σ i = 0 M - 1 b i q ( n - i Q ) Σ j = 0 N - 1 h ( n - j P ) + w ( n )
h ( n ) = Σ k = 0 P - 1 c k p ( n - k )
A, b thereini(bi∈ { ± 1}, i ═ 0., M-1, and ck(ck∈ { ± 1}, k 0., P-1) represent the amplitude of the signal, the information code sequence, and the spreading code sequence, respectively, M represents the number of information codes in NP signal samples q (n) is a rectangular function and satisfiesp (n) denotes the convolution of the transmit filter, channel and receive filter, h (n) is the spreading code sequence ckAnd an impulse response of p (n), w (n) representing additive white gaussian noise.
Step 102: calculating the number of information symbols contained in a spreading code period The expression is rounded up, then in a long code spreading period, the information code element has β jump points at most, and a jump mode matrix is constructed
Wherein Hk(k 0.. β) represents a hopping pattern matrix with k hopping points corresponding to the hopping pattern matrixkA jump position is denoted by 1 to-1 or-1 to 1. k-0 represents no jumping point, corresponding to all 1 row vectors with length P, k-1 represents only one jumping point, and the jumping position at one jumping point can occur at any front and back adjacent chips in P chipsThe position between the two, P-1 row vectors in the worst case, and so on, thereby determining the whole information code hopping matrixThe hopping pattern matrix can also be approximately expressed by a matrix formed by the hopping patterns of a plurality of hopping points with the maximum hopping probability, namely, the information code hopping matrix can be usedTo reduce the dimensionality of the matrix.
Step 103: segmenting the received baseband signal with the long code with noise according to the period P to obtain N vectors y with the length of Pl(l＝0，...，N-1)
Wherein,representing a sequence of information symbols sampled at the chip rate.
OperatorRepresentation matrixThe elements in each row in (1) and ylThe corresponding elements in the vector are dot multiplied, ⊙ indicates the elements at the corresponding positions are dot multipliedMust have a group of hopping patterns and blThe hopping patterns of the medium information codes are the same, i.e. Is a matrix of × P, thenCan be expressed as:
s.t.||bl(1：P)||2＝P
| b l ( i ) | = 1 , ∀ i ∈ { 0 , 1 , ... P - 1 }
step 104: according toComputing a covariance matrixTo pairDecomposing the eigenvalue to obtain corresponding eigenvector wmaxThereby obtaining an estimated spreading code sequence
c ^ = s i g n ( w max )
Where sign (·) represents a sign function.
Step 105: the estimated spread spectrum code sequenceCorrelating with the sampling sequence y (n) to recover the information code element sequence bi(bi∈{±1}，＝0，...，M-1)。
As shown in fig. 2, the performance comparison graph of the present invention and the conventional eigenvalue decomposition method varying with the sampling period length, in the embodiment, the spreading code period P is 127, the information symbol width Q is 60, the signal sample length N is set to be between 20 and 200 spreading periods, the signal-to-noise ratio is-10 dB, 200 monte carlo simulations are performed, and the performance comparison of the symbol error rate is performed between the present invention and the conventional eigenvalue decomposition method without information code hopping elimination. As can be seen from the embodiment in fig. 2, the present invention requires fewer samples under the same performance, and is very suitable for the estimation of the spreading sequence in the case of fewer intercepted samples.
As shown in the figure3, the comparison graph of the related coefficient of despreading spread spectrum code with the change of signal-to-noise ratio by the traditional eigenvalue decomposition is shown, the length N of the signal sample is set as 100 under different signal-to-noise ratios, the signal-to-noise ratio is stepped from-15 dB to 0dB according to 1dB, the period P of the spread spectrum code and the width Q of the information code element are respectively 127 and 60, the performance of the parameter of the spread spectrum code estimated by the invention and the traditional eigenvalue decomposition without information code hopping is compared, and the performance is estimated by defining the estimated spread spectrum code sequenceNormalized correlation coefficient with actual spreading code cTo characterize. As can be seen from fig. 3, the performance improvement of the present invention for estimating spreading codes is significant.
As shown in fig. 4, the error rate of the information code recovered by the invention and the conventional eigenvalue decomposition and cooperative despreading vary with the signal-to-noise ratio, and the error rate of the information code recovered by the invention and the conventional eigenvalue decomposition without information code hopping elimination are compared with the performance under cooperative communication conditions under different signal-to-noise ratios. The parameter setting is consistent with the embodiment of fig. 3, and it can be seen from fig. 4 that the present invention converges to the performance of cooperative communication more quickly.

Claims (6)

1. The method for blind estimation of the long code DSSS signal for eliminating the jump of the information code is characterized by comprising the following steps:
a. obtaining a baseband sampling signal by down-converting an air interface signal, obtaining a long code spread spectrum DSSS signal period P, a chip rate and an information code element time width Q by blind estimation, and intercepting a digital signal with the length of N segments being P from the sampling signal to obtain the following expression
y ( n ) = A Σ i = 0 M - 1 b i q ( n - i Q ) Σ j = 0 N - 1 h ( n - j P ) + w ( n )
h ( n ) = Σ k = 0 P - 1 c k p ( n - k )
Wherein A represents the amplitude of the signal, bi(bi∈ { ± 1}, i ═ 0., M-1) and ck(ck∈ { ± 1}, k 0., P-1) represents an information code sequence and a spreading code sequence, respectively, M represents the number of information codes in NP signal samples, and q (n) is a rectangular function and satisfiesp (n) represents a transmission filter,Convolution of the channel with the receive filter, h (n) being the spreading code sequence ckAnd an impulse response of p (n), w (n) representing additive white gaussian noise;
b. calculating the number of information code elements contained in a spreading code period according to the estimated time width Q of the information code elements and the spreading code period P The mathematical sign represents rounding up, so that in a long code spreading period, the information code element has β jump points at most to construct a jump mode matrix
Wherein Hk(k 0.., β) represents a hopping pattern matrix with k hopping points corresponding to each other;
c. segmenting the received baseband signal with the long code with noise according to the period P to obtain N vectors y with the length of Pl(l ═ 0.., N-1), where
Wherein,representing a sequence of information symbols sampled at a chip rate;
d. computingIn thatMust have a group of hopping patterns and blThe hopping patterns of the medium information codes are the same, i.e. Is a matrix of × P, thenCan be expressed as
s.t.||bl(1：P)||2＝P
| b l ( i ) | = 1 , ∀ i ∈ { 0 , 1 , ... P - 1 }
e. According toComputing a covariance matrixTo pairPerforming principal component decomposition to obtain corresponding feature vector wmaxThereby obtaining an estimated spreading code sequence
c ^ = s i g n ( w m a x )
Where sign (·) represents a sign function.
2. The method for blind estimation of long-code DSSS signals with information code hopping elimination according to claim 1, wherein in the step a, the period P of the blind estimation of the long-code spread spectrum DSSS signals is determined by quadratic spectrum, correlation fluctuation spectrum, cepstrum, cyclic spectrum or autocorrelation difference.
3. The method of blind estimation of long code DSSS signal with information code hopping elimination as claimed in claim 1, wherein in step b, the submatrix H in hopping pattern matrixk1 to-1 or-1 to 1 is used for representing a jump position, k is 0 for representing no jump point, corresponding to all 1 row vectors with the length of P, k is 1 for representing only one jump point, the jump position can occur at the position between any front and back adjacent chips in P chips when one jump point is formed, P-1 row vectors are in the worst case, and the like, thereby determining the whole information code jump matrix
4. The method for blind estimation of long code DSSS signals with information code hopping elimination as claimed in claim 3, wherein in said step b, the hopping pattern matrix can be selected from hopping patternsThe matrix formed by the hopping patterns of the several hopping points with the maximum probability is approximately expressed, namely the information code hopping matrixTo reduce the dimensionality of the matrix.
5. The method for blind estimation of long code DSSS signals with information code hopping elimination as claimed in claim 1, wherein in said step d, the operatorRepresentation matrixThe elements in each row in (1) and ylThe corresponding elements in the vector are dot multiplied.
6. The method for blind estimation of a long code DSSS signal with information code hopping elimination according to claim 1, wherein in step e, the principal component decomposition is performed by singular value decomposition or eigenvalue decomposition.
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CN112702080A (en) * 2020-12-16 2021-04-23 中国人民解放军国防科技大学 Direct sequence spread spectrum signal and pseudo code estimation method based on K-means algorithm
CN115940992A (en) * 2022-11-16 2023-04-07 中国人民解放军战略支援部队航天工程大学 BL-DSSS signal code tracking method based on frequency domain subspace principle

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107682041A (en) * 2017-10-19 2018-02-09 杭州电子科技大学 A kind of multiple antennas LC DS CDMA signal spread-spectrum code blind estimating methods
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CN112702080A (en) * 2020-12-16 2021-04-23 中国人民解放军国防科技大学 Direct sequence spread spectrum signal and pseudo code estimation method based on K-means algorithm
CN115940992A (en) * 2022-11-16 2023-04-07 中国人民解放军战略支援部队航天工程大学 BL-DSSS signal code tracking method based on frequency domain subspace principle
CN115940992B (en) * 2022-11-16 2023-10-03 中国人民解放军战略支援部队航天工程大学 BL-DSSS signal code tracking method based on frequency domain subspace principle

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