CN106817326B - A Pseudo-Code Blind Estimation Method for Multi-User Periodic Long-Short-Short Code Direct Spread Signals - Google Patents

Abstract

The invention discloses a pseudo code blind estimation method of a multi-user long-period short code direct sequence spread spectrum signal. According to the period of short spread codes and long scrambling codes, the invention models a multi-user long-period short code direct spread signal with a complex structure into a blind separation form; separating mixed PN sequence segments of each user based on a Fast-ICA algorithm, and recombining the mixed PN sequence segments into a fuzzy sequence; the influence of information codes and spread spectrum codes is eliminated by utilizing the shift superposition of the m sequence and adopting secondary delay phase multiplication; and calculating a third-order correlation function of the signal by combining a cyclotomic coset theory, thereby determining a mixed PN sequence and a third-order correlation peak point of a user, completing scrambling code estimation by using a matrix skew elimination method, and completing spread spectrum code estimation by using a piecewise delay cross-correlation method after descrambling the signal. The invention fully utilizes Fast-ICA algorithm and m sequence third order correlation characteristic to realize blind estimation of two pseudo-random codes of multi-user long-period short code direct sequence spread spectrum signals.

Description

A Pseudo-Code Blind Estimation Method for Multi-User Periodic Long-Short-Short Code Direct Spread Signals

technical field

The invention belongs to the field of blind parameter estimation of direct sequence spread spectrum signals in communication countermeasures, and particularly relates to a pseudo code estimation method for multi-user periodic direct spread signals scrambled by short code spread spectrum long codes in non-cooperative communication.

Background technique

Direct Sequence Spread Spectrum (Direct Sequence Spread Spectrum, DSSS) is one of the main methods of spread spectrum communication technology. It has the advantages of strong anti-interference ability, good confidentiality, easy code division multiple access, etc., and has a wide range of applications in military and civilian communications. According to the signal structure, DSSS signal can be divided into: short code direct spread signal, long code direct spread signal, short code spread spectrum long code scrambled direct spread signal (referred to as long and short code direct spread signal).

In the communication confrontation, it is precisely because of the strong anti-interference ability and good concealment of the direct spread signal, which makes the direct spread signal detection and blind parameter estimation in the case of non-cooperative communication quite difficult. In non-cooperative communication, pseudo-random (PN) code estimation is the premise and key to information interception. The research on PN code estimation of short code direct spread signal has been relatively mature, and the research on PN code estimation of long code direct spread signal has also achieved certain results. However, due to the complex structure and stronger confidentiality of the long-short code direct spread signal, it brings greater difficulties and challenges to the PN code estimation.

The existing methods for estimating the spreading code of the direct spread signal mainly include: the correlation matrix eigendecomposition method, the neural network method, the matched filtering method and the three-boundary correlation method. For a multi-user long and short code direct spread signal, which not only includes multiple users, but also includes two pseudo codes in each user, the above methods are not applicable. At present, the research on pseudo-code estimation of multi-user long-short-code direct spread signals is still in its infancy and needs to be further explored.

SUMMARY OF THE INVENTION

The purpose of the present invention is to propose a pseudo-random code blind estimation method based on Fast-ICA algorithm and third-order correlation for the pseudo-code problem that multi-user periodic long-short code direct spread signals cannot be estimated in non-cooperative communication, thereby solving the problem of multi-user Pseudo-code estimation problem for direct-spread signals with long and short period codes.

In the present invention, the steps of the pseudo-code blind estimation method for multi-user period long-short code direct spread signals are:

Step 1. Convert the multi-user period long-short code direct-spread signal into a baseband signal by sampling at the spreading code chip rate, segment the baseband signal according to the short spreading code and long scrambling code period and construct a blind separation model. The component analysis (Fast-ICA) algorithm is used to separate the mixed PN sequence fragments of each user;

Step 2. When estimating the mixed PN sequence of the first user, splicing and separating the obtained sequence fragments to obtain a complete mixed PN fuzzy sequence, using the shift and superposition of the m sequence, and using the second delay phase multiplication to eliminate the information code and spread spectrum code impact;

Step 3. Calculate the third-order correlation function value of the delayed signal in combination with the theory of the coset of the circle, and obtain the possible peak point coordinates;

Step 4. Calculate the average value of the forward and reverse third-order correlation functions at the coordinate set of possible peak points, and the sequence corresponding to the maximum average value is the correct user mixed PN sequence;

Step 5, complete the scramble code estimation of the user according to the matrix oblique elimination method, descramble the mixed PN sequence of the user, and complete the spread spectrum code estimation with the segmented delay cross-correlation method;

Step 6: Clear all the mixed PN sequence fragments of the estimated users in step 1, and repeat steps 2-5 to obtain the mixed PN sequences, scrambling codes and spreading codes of all users in sequence.

The invention makes full use of the structural characteristics of the multi-user period long-short code direct spread signal, constructs a blind separation model through signal segmentation, and realizes the separation of mixed PN sequence segments of each user through the Fast-ICA algorithm.

In the present invention, all the obtained mixed PN sequence fragments are sequentially spliced to obtain all possible mixed PN fuzzy sequences, and the correct user mixed PN sequence must exist therein.

The present invention eliminates the influence of the information code and the influence of the spreading code through two delays and multiplications, so that the scrambling code can be estimated through the third-order correlation characteristic. The third-order correlation function of the signal is calculated in combination with the theory of the coset of the circle to determine the possible peak points, which greatly reduces the computational complexity and the amount of computation.

In order to reduce the influence of noise on pseudo code estimation, the invention uses the properties of peak points to calculate the average value of forward and reverse third-order correlation functions at the coordinates of possible peak points, thereby improving the search accuracy of the correct mixed PN sequence.

In the present invention, each peak point is expressed as a polynomial form, and the greatest common formula is obtained in pairs, then the primitive polynomial estimation of the signal long scrambling code can be obtained.

After estimating the long scrambling code of the signal, the present invention utilizes the linear shift and superposition characteristic of the m sequence to subdivide ingeniously, uses the subsection correlation method to descramble the signal, and then realizes the spread spectrum through the signal synchronization method of subsection delay cross-correlation. code estimation.

The beneficial effects of the present invention are:

1. The multi-user period long and short code direct spread signal is modeled as the problem of blind source signal separation, and the complex signal model can be simplified by this modeling.

2. The influence of information code and spread spectrum code on the estimation of long scrambling code is eliminated through two delay multiplications. By using the third-order correlation characteristics of m-sequence, the theory of coset circles, and the properties of peak points, not only the amount of computation is reduced, but also the It also greatly improves the accuracy of determining the hybrid PN sequence, thereby improving the scrambling code estimation performance.

3. After the mixed PN sequence is descrambled, the short-spreading code estimation is completed by the signal synchronization method of segmental delay cross-correlation, and the estimation of the long scrambling code and the short spreading code of each user in the signal is finally realized.

Detailed ways

The implementation steps of the present invention are further described in detail below.

A pseudo-code blind estimation method for a multi-user period long and short code direct spread signal specifically includes the following steps:

Step 1. Convert the multi-user period long-short code direct-spread signal into a baseband signal by sampling at the spreading code chip rate, segment the baseband signal according to the short spreading code and long scrambling code period and construct a blind separation model. The component analysis (Fast-ICA) algorithm is used to separate the mixed PN sequence fragments of each user; the details are as follows:

1-1. Sampling the received multi-user periodic long-short code direct spread signal at the spreading code chip rate, the baseband signal of the i-th user is expressed as:

u _i (n)=A _i d _i (n)h _i (n)k _i (n),n=1,2,3...N (1)

Among them, n is the sampling time, N is the length of the baseband signal; A _i is the signal amplitude of the ith user; d _i (n), _hi (n), and _ki (n) represent the information code of the ith user, respectively , spreading codes and scrambling codes. The spreading code selects an OVSF code with a period of L _s , and the scrambling code selects an m sequence with a period of L _l , and satisfies the condition L _l =VL _s , where V is a positive integer. All users have the same spreading code period and scrambling code period, then each user signal can be divided into Z=N/L _l segments.

The multi-user period long and short code direct spread signal is expressed as:

where M is the number of users, w(n) is zero-mean Gaussian white noise, and the variance is σ ² .

1-2. According to the short spreading code and long scrambling code period, the baseband signal is segmented and constructed into a blind separation model, as follows:

Firstly, according to the scrambling code period, the received signal is divided into Z=N/L _l sequence segments, each sequence segment has a length of L _l , which can be regarded as signals received by Z array elements. Then, segment the signal of each array element according to the period of the spreading code to obtain V=L _l /L _s sequence segments with a length of L _s . Then the received signal of the vth segment can be expressed as:

Build a blind separation model:

r(v,a)=A(v)B(v,a)+W(v,a),a=1,2,3,...,L _s (4)

in

B(v,a)=[s ₁ (v,a) s ₂ (v,a) … s _M (v,a)] ^T ; s _i (v,a)= _{hi (a)k i (} ( v-1) · L _s + a);

W(v,a)=[w ₁ ((v-1) · L _s +a) w ₂ ((v-1) · L _s +a) … w _Z ((v-1) · L _s +a )] ^T ;

i＝1,2,…M，v＝1,2,…V。The Fast-ICA algorithm is used to separate the mixed PN sequence fragments of each user, which is denoted as

i=1,2,...M, v=1,2,...V.

Step 2. When estimating the mixed PN sequence of the first user, splicing and separating the obtained sequence fragments to obtain a complete mixed PN fuzzy sequence, using the shift and superposition of the m sequence, and using the second delay phase multiplication to eliminate the information code and spread spectrum code impact. details as follows:

2-1. _{Mv different fuzzy sequences α t} ⁽ n) can be obtained by splicing the sequence fragments separated in step 1 in order, ^t =1,2,...MV , the length is L _l , α _t ( n) is expressed as:

where i ₁ , i ₂ ,...i _V =1,2,...M. All α _t (n) are formed by concatenating V consecutive sub-segments, but only M are the required user mixed PN sequences, and these M special sequences are denoted as

为用户的第一个序列片段，则该用户的

可由(V-1)M个剩余子段

来拼接，其中i₂,…i_V＝1,2,…M，

中的t＝1,2,…M^V-1。2-2. Estimate the mixed PN sequence of the first user, assuming

is the first sequence fragment of the user, then the user's

There are (V-1)M remaining sub-segments

to splicing, where i ₂ ,...i _V =1,2,...M,

t=1,2,... ^MV-1 in .

2-3. In order to eliminate the influence of the information code and the spreading code on the estimation of the scrambling code, the shift and superposition characteristics of the m sequence are used to multiply the delays twice:

Step 3: Calculate the value of the third-order correlation function of the delayed signal in combination with the theory of the coset circle, and obtain the possible coordinates of the peak point.

的正向三阶相关函数(TCF)，其中t＝1,2,…M^V-1：Determine the circular coset of the scrambling code, assuming that there are J finite sets, record the coset heads of these J finite sets into the set {η _j |j=1,2,3,...,J}. Calculate the delayed signal obtained by multiplying the delays

The forward third-order correlation function (TCF) of , where t=1,2,...M ^V-1 :

对应的坐标，即最有可能的TCF峰值点坐标，记入集合

where j=1,2,...J, q=1,2,...L _l , find the J largest

The corresponding coordinates, that is, the most likely coordinates of the TCF peak point, are recorded in the set

Step 4. Calculate the average value of the forward and reverse third-order correlation functions at the coordinate set of possible peak points, and the sequence corresponding to the maximum average value is the correct user mixed PN sequence;

The m-series forward and reverse third-order correlation functions have the property of C ⁺ (p,q)=C ^- (p,pq) (where p>q), that is, if (p,q) is the forward TCF peak point, then ( p,pq) are the coordinates of the inverse TCF peak point. In order to reduce the influence of noise on scrambling code estimation, this property can be used to improve the search accuracy of the PN mixed sequence, and the average value of the forward and reverse third-order correlation functions is calculated by the following formula:

Step 5, complete the user's scrambling code estimation according to the matrix oblique elimination method, descramble the mixed PN sequence, and complete the spread spectrum code estimation with the segmented delay cross-correlation method;

其对应的模糊序列就是要求的第一个用户的混合PN序列。将求得的混合PN序列对应的每个峰值点表示为多项式形式，并两两求最大公约式，从而得到信号长扰码的本原多项式估计。随后对混合PN序列解扰，并用分段延迟互相关的信号同步法完成对短扩频码估计。find the largest

Its corresponding fuzzy sequence is the required mixed PN sequence of the first user. Each peak point corresponding to the obtained mixed PN sequence is expressed as a polynomial form, and the greatest common formula is obtained in pairs, so as to obtain the original polynomial estimation of the signal long scrambling code. Then the mixed PN sequence is descrambled, and the short spreading code estimation is completed by the signal synchronization method of piecewise delay cross-correlation.

Step 6: Clear all the mixed PN sequence fragments of the estimated users in step 1, and repeat steps 2-5 to obtain the mixed PN sequences, scrambling codes and spreading codes of all users in sequence.

和

其中t＝1,2,…(M-1)^V-1，得到第二个用户的混合PN序列，再通过步骤3-5估计出第二个用户的扰码和扩频码。重复以上过程即可得到所以用户的PN混合序列、扰码和扩频码，最终完成多用户周期长短码直扩信号的伪码估计。Reconstruct (M-1) ^V-1 possible by step 2

and

where t=1,2,...(M-1) ^V-1 , obtain the mixed PN sequence of the second user, and then estimate the scrambling code and spreading code of the second user through steps 3-5. By repeating the above process, the PN mixed sequence, scrambling code and spreading code of all users can be obtained, and finally the pseudo code estimation of the multi-user periodic long and short code direct spread signal is completed.

Claims (5)

1. the pseudo-code blind estimation method of multi-user period long and short code direct spread signal, is characterized in that this method may further comprise the steps: Step 1. Convert the multi-user period long-short code direct-spread signal into a baseband signal by sampling at the spreading code chip rate, segment the baseband signal according to the short spreading code and long scrambling code period and construct a blind separation model. The component analysis algorithm separates and obtains the mixed PN sequence fragments of each user; Step 2. When estimating the mixed PN sequence of the first user, splicing and separating the obtained sequence fragments to obtain a complete mixed PN fuzzy sequence, using the shift and superposition of the m sequence, and using the second delay phase multiplication to eliminate the information code and spread spectrum code impact; Step 3. Calculate the third-order correlation function value of the delayed signal in combination with the theory of the coset of the circle, and obtain the possible peak point coordinates; Step 4. Calculate the average value of the forward and reverse third-order correlation functions at the coordinate set of possible peak points, and the sequence corresponding to the maximum average value is the correct user mixed PN sequence; Step 5, complete the scramble code estimation of the user according to the matrix oblique elimination method, descramble the mixed PN sequence of the user, and complete the spread spectrum code estimation with the segmented delay cross-correlation method; Step 6: Clear all the mixed PN sequence fragments of the estimated users in step 1, and repeat steps 2-5 to obtain the mixed PN sequences, scrambling codes and spreading codes of all users in sequence. 2. the pseudo-code blind estimation method of the multi-user period long and short code direct spread signal according to claim 1, is characterized in that step 1 specifically comprises the following content: 1-1. Sampling the received multi-user periodic long-short code direct spread signal at the spreading code chip rate, the baseband signal of the i-th user is expressed as: u _i (n)=A _i d _i (n)h _i (n)k _i (n),n=1,2,3...N (1) Among them, n is the sampling time, N is the length of the baseband signal; A _i is the signal amplitude of the ith user; d _i (n), _hi (n), and _ki (n) represent the information code of the ith user, respectively , spread spectrum code and scrambling code; the spread spectrum code selects the OVSF code with a period of L _s , and the scrambling code selects the m sequence with a period of L _l , and satisfies the condition L _l =VL _s , where V is a positive integer; all users have The same spreading code period and scrambling code period, then each user signal can be divided into Z=N/L _l segments; The multi-user period long and short code direct spread signal is expressed as:

where M is the number of users, w(n) is zero-mean Gaussian white noise, and the variance is σ ² ; 1-2. According to the short spreading code and long scrambling code period, the baseband signal is segmented and constructed into a blind separation model, as follows: First, according to the scrambling code period, the received signal is divided into Z=N/L _l sequence segments, each sequence segment has a length of L _l , which can be regarded as the signal received by Z array elements; The array element signal is segmented, and V=L _l /L _s sequence segments are obtained, and the length is L _s ; then the received signal of the v-th segment can be expressed as:

Build a blind separation model: r(v,a)=A(v)B(v,a)+W(v,a),a=1,2,3,...,L _s (4) in

B(v,a)=[s ₁ (v,a) s ₂ (v,a) … s _M (v,a)] ^T ; s _i (v,a)= _{hi (a)k i (} ( v-1) · L _s + a); W(v,a)=[w ₁ ((v-1) · L _s +a) w ₂ ((v-1) · L _s +a) … w _Z ((v-1) · L _s +a )] ^T ; The Fast-ICA algorithm is used to separate the mixed PN sequence fragments of each user, which is denoted as

i=1,2,...,M, v=1,2,...,V. 3. the pseudo-code blind estimation method of the multi-user period long and short code direct spread signal according to claim 2, is characterized in that in step 2 is as follows: 2-1. The sequence fragments separated in step 1 are spliced in order to obtain M ^V fuzzy sequences α _t (n) with a sequence length of L _l , where M is the number of users, V is the number of sequence fragments, t=1,2,..., _{MV , α t} ⁽ n) is expressed as:

in

Indicates the v-th mixed PN sequence fragment obtained by separation, i _v =1,2,...,M,v=1,2,...,V; all α _t (n) are composed of V consecutive sub-segments spliced , but only M are the required user mixed PN sequences, and these M special sequences are denoted as

2-2. Estimate the M special sequences

The mixed PN sequence of the first user in , denoted as

Assumption

is the first sequence fragment of the user, then the user's

There are (V-1)M remaining sub-segments

splicing, i _v =1,2,...,M,v=2,...,V; where

in t=1,2,... ^MV-1 ; 2-3. In order to eliminate the influence of the information code and the spreading code on the estimation of the scrambling code, the shift and superposition characteristics of the m sequence are used to multiply the delays twice:

4. the pseudo-code blind estimation method of the multi-user period long and short code direct spread signal according to claim 3, it is characterized in that step 3 is specifically as follows: Determine the circular coset of the scrambling code, assuming that there are J finite sets, record the coset heads of these J finite sets into the set {η _j | j = 1, 2, 3, ..., J}; calculate the delay multiplication The resulting delayed signal

The forward third-order correlation function (TCF) of , where t=1,2,...M ^V-1 :

where j=1,2,...J, p=1,2,...L _l , q=1,2,...L _l , find the J largest

The corresponding coordinates, that is, the most likely coordinates of the TCF peak point, are recorded in the set

5. the pseudo-code blind estimation method of multi-user period long and short code direct spread signal according to claim 4, is characterized in that in step 4 and step 5: In step 4, in order to reduce the influence of noise on the estimation of the scrambling code, the average value of the forward and reverse third-order correlation functions is calculated by using the characteristics of the third-order correlation function of the m sequence:

find the largest

Its corresponding fuzzy sequence is the mixed PN sequence of the first user; In step 5, each peak point corresponding to the obtained sequence is expressed as a polynomial form, and the greatest common formula is obtained in pairs, and the matrix oblique elimination method is used to complete the original polynomial estimation of the signal long scrambling code; then the mixed PN sequence is descrambled, And the estimation of short spreading codes is completed by the signal synchronization method of piecewise delay cross-correlation.