Cluster signal identification processing method and system based on high-order cumulant characteristics

Abstract

The invention relates to a cluster signal identification processing method and system based on high-order cumulant characteristics, and the method comprises the steps: 1, analyzing the anti-fading performance of the high-order cumulant characteristics, and obtaining the correlation characteristics of the high-order cumulant characteristics and the multipath interference capability; wherein the correlation characteristic is as follows: along with the increase of the high-order cumulant, the capability of suppressing the multipath interference is stronger; and 2, receiving a signal sequence, calculating a fourth-order cumulant of the signal, constructing a fourth-order cumulant feature vector based on the fourth-order cumulant, receiving a classification feature vector extracted from the signal sequence, and performing judgment by using an Euclidean distance classifier to obtain a signal identification result. The method not only improves the recognition rate of the cluster signal, but also effectively overcomes the influence of the multipath channel on the classification performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal recognition, and in particular relates to a cluster signal recognition and processing method and system based on high-order cumulant features. Background Art

[0002] Cluster signal recognition refers to the identification of modulation modes and characteristics of signals in drone cluster communications in a complex electromagnetic environment. These illegal cluster signals pose challenges to electromagnetic spectrum management, so identifying these signals is crucial to maintaining radio order. The development of cluster signal recognition technology is closely related to the standardization of digital cluster communication systems. Mainstream digital cluster communication systems include TETRA, P25, DMR, dPMR, etc. These standards define basic characteristics of cluster signals such as time-frequency characteristics and modulation parameters. With the development of technology, cluster signal recognition technology is also constantly improving, such as narrowing the recognition range based on basic characteristics such as time-frequency characteristics and modulation parameters of cluster signals. Existing cluster signal recognition technology has technical problems such as low recognition rate and the impact of multipath channels on classification performance. Summary of the invention

[0003] The purpose of the present invention is to provide a cluster signal recognition and processing method and system based on high-order cumulative amount characteristics, which can recognize cluster signals in a fading channel environment, has good anti-fading characteristics, improves the recognition rate of cluster signals, and effectively overcomes the influence of multipath channels on classification performance.

[0004] The present invention provides a cluster signal recognition and processing method based on high-order cumulant features, comprising the following steps:

[0005] Step 1, analyzing the anti-fading performance of the high-order cumulative amount characteristic to obtain the correlation characteristics of the high-order cumulative amount and the ability of multipath interference; wherein the correlation characteristics are: as the high-order cumulative amount increases, the ability to suppress multipath interference is stronger;

[0006] Step 2, receiving the signal sequence, calculating the fourth-order cumulant of the signal, constructing a fourth-order cumulant feature vector based on the fourth-order cumulant, extracting the classification feature vector from the received signal sequence, using the Euclidean distance classifier for judgment, and obtaining a signal recognition result.

[0007] Furthermore, the step 1 comprises:

[0008] Assume that the P-order cumulant of the signal sequence s(k) is expressed as c s,pq (l1,l1,…,l p ), where l1,l1,…,l pis the delay value of each sequence, q (q≤p / 2) represents the number of conjugated sequences; because the code element sequences are independent and identically distributed, their high-order cumulants are only in l1,l1,…,l p =0, there is a non-zero value, and we get:

[0009]

[0010] In the formula, c r,pq represents the P-order cumulant of the received signal r(k), l1,l1,…,l p =0, q is the number of sequences to be conjugated, and the channel effect is removed by dividing the two cumulative values. Let:

[0011]

[0012] f is used as the modulation classification characteristic value, which is called the cumulative characteristic. When there is only one path, that is, when the multipath number L+1 is 1, it is obtained:

[0013]

[0014] The influence of the channel is eliminated by taking the modulus of the numerator and denominator respectively, and the eigenvalues ​​of f after passing through the channel and not passing through the channel are equal; in the multipath channel environment, the number of sequences taken conjugate in the cumulative expression is different, and multiple complex numbers are no longer equal after taking the modulus, and the multipath channel affects the eigenvalue f; assuming that h0 is the channel response value of the maximum modulus, when satisfying When , the multipath effect is ignored;

[0015] Assume there exists a constant R,

[0016]

[0017] After transformation:

[0018]

[0019] Among them, h max / h0 is the amplitude attenuation coefficient of the multipath channel;

[0020] According to the ratio of the sub-maximum diameter to the maximum diameter in the 4th, 6th, and 8th order cumulants when R is 5 and 10 respectively, it is found that the ability to suppress multipath interference is greatly improved as the order of the high-order cumulants increases.

[0021] Furthermore, the step 2 comprises:

[0022] The calculation formula of the fourth-order cumulant of the signal, moment-cumulant formula:

[0023]

[0024] In the formula, represents the summation within all partitions of I;

[0025] For a zero-mean complex random sequence, its fourth-order cumulant is expressed as follows:

[0026] c s,40 =cum[s(k),s(k),s(k),s(k)]=E[s(k) 4 ]-3E[s(k) 2 ] 2

[0027] c s,41 = cum[s(k),s(k),s(k),s * (k)] = E[s(k) 3 s * (k)]-3E[s(k) 2 ]E[s(k)s * (k)]

[0028] c s,42 = cum[s(k),s(k),s(k),s * (k)]

[0029] =E[s(k) 2 s * (k) 2 ]-E[s(k) 2 ] 2 -2E[s(k)s * (k)] 2

[0030] Where cum[·] represents the cumulative amount calculation expression, s * (k) represents the conjugation of the sequence, and E[·] represents the mathematical expectation;

[0031] Obtain theoretical values ​​of high-order cumulative quantities for each modulation mode;

[0032] The 4th-order cumulant eigenvector is constructed as follows:

[0033] f s,1 =|c s,41 | / |c s,42 |

[0034] f s,2 =|c s,40 | / |c s,42 |

[0035] For signals of different modulation orders, the fourth-order cumulant classification feature vector has the following form:

[0036]

[0037] When selecting a classifier, use the Euclidean distance classifier, and the decision criteria are:

[0038]

[0039] In the formula, represents the classification feature vector extracted from the received signal sequence, Represents the distance between the received signal classification feature vector and the feature vector of a certain modulation method in the type set;

[0040] The trunking signal sequence is fed into the recognition process to identify the modulation mode, and further demodulate to find the TETRA synchronization code.

[0041] The present invention also provides a cluster signal recognition and processing system based on high-order cumulant features, comprising a recognition and processing module, wherein the recognition and processing module executes the cluster signal recognition and processing method based on high-order cumulant features.

[0042] The present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the cluster signal recognition and processing method based on high-order cumulative quantity features is implemented.

[0043] The present invention also provides an electronic device, comprising:

[0044] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the cluster signal recognition and processing method based on high-order cumulative quantity features by executing the computer instructions.

[0045] By means of the above scheme, through the cluster signal recognition processing method and system based on high-order cumulant features, not only the recognition rate of cluster signals is improved, but also the influence of multipath channels on classification performance is effectively overcome.

[0046] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the cluster signal recognition and processing method based on high-order cumulant features of the present invention;

[0048] Figure 2 This is a flow chart of a signal recognition method based on fourth-order cumulants in one embodiment of the present invention;

[0049] Figure 3 A schematic diagram of TETRA synchronization code in an embodiment of the present invention;

[0050] Figure 4 The figure is a schematic diagram of an electronic device according to the present invention. DETAILED DESCRIPTION

[0051] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0052] Ginseng Figure 1 As shown, this embodiment provides a cluster signal recognition and processing method based on high-order cumulant features, comprising the following steps:

[0053] Step S1, analyzing the anti-fading performance of the high-order cumulative amount characteristics to obtain the correlation characteristics of the high-order cumulative amount times and the ability of multipath interference; wherein the correlation characteristics are: as the high-order cumulative amount times increases, the ability to suppress multipath interference is stronger;

[0054] Step S2, receiving a signal sequence, calculating the fourth-order cumulant of the signal, constructing a fourth-order cumulant feature vector based on the fourth-order cumulant, receiving a classification feature vector extracted from the signal sequence, using a Euclidean distance classifier for judgment, and obtaining a signal recognition result.

[0055] The present invention is described in further detail below.

[0056] The instantaneous characteristic high-order accumulation of the signal can reflect the second-order statistical characteristics of the signal, and the modulation characteristics of the signal are also reflected in the high-order statistical characteristics of the signal. Although the calculation of high-order statistics is more complicated, the appropriate high-order statistics have good anti-fading characteristics, which is not available in instantaneous statistics. Therefore, when identifying cluster signals in a fading channel environment, the performance advantage of the recognition method based on high-order statistics is more obvious.

[0057] The performance analysis of cluster signal recognition and processing methods based on high-order cumulants is not only about recognition rate, but also about anti-fading performance. First, the anti-multipath interference performance of high-order cumulants should be analyzed, and on this basis, a cluster signal modulation recognition method based on high-order cumulants is given.

[0058] 1) High-order cumulant characteristics for anti-multipath fading interference performance

[0059] Assume that the P (even) order cumulant of the signal sequence s(k) is expressed as c s,pq (l1,l1,…,l p ), where l1,l1,…,l p, represents the delay value of each sequence, and q (q≤p / 2) represents the number of conjugated sequences. Because the code element sequences are independent and identically distributed, their high-order cumulants are only in l1,l1,…,l p = 0, it has a non-zero value. We can get:

[0060]

[0061] In the formula, c r,pq represents the P-order cumulant of the received signal r(k), l1,l1,…,l p =0, q is the number of sequences to be conjugated, and the channel effect can be removed by dividing the two cumulative values. Let:

[0062]

[0063] f can be used as a modulation classification feature value, called the cumulative feature. When there is only one path, that is, when the multipath number L+1 is 1, it can be obtained:

[0064]

[0065] It can be seen that the influence of the channel is eliminated by taking the modulus of the numerator and denominator respectively, and the eigenvalues ​​of f after passing through the channel and not passing through the channel are equal. However, in a multipath channel environment, the number of sequences taken conjugate in the cumulative expression is different, and multiple complex numbers are no longer equal after taking the modulus, so the multipath channel will affect the eigenvalue f. Assuming h0 is the channel response value of the maximum modulus value, then when satisfying

[0066] When , the multipath effect can be ignored.

[0067] Assume there exists a constant R,

[0068]

[0069] After transformation:

[0070]

[0071] where h max / h0 is the amplitude attenuation coefficient of the multipath channel. The following table shows the ratio of the sub-maximum diameter to the maximum diameter in the case of 4th, 6th, and 8th order cumulants when R is set to 5 and 10 respectively. The values ​​of the amplitude attenuation coefficient of the multipath channel in the most severe deterioration are shown in Table 1.

[0072] Table 1 Multipath channel amplitude attenuation coefficient values ​​under the most severe deterioration

[0073]

[0074]

[0075] It can be seen that with the increase of the order of high-order cumulants, the ability to suppress multipath interference has been greatly improved.

[0076] 2) Cluster signal recognition method based on high-order cumulants

[0077] From the above analysis, it can be seen that the higher the order of the cumulant, the stronger its ability to resist multipath interference, but the corresponding computational complexity is also higher.

[0078] The calculation formula of the fourth-order cumulant of the signal, moment-cumulant formula:

[0079]

[0080] In the formula, represents the summation within all partitions of I.

[0081] For a zero-mean complex random sequence, its fourth-order cumulant is expressed as follows:

[0082] c s,40 =cum[s(k),s(k),s(k),s(k)]=E[s(k) 4 ]-3E[s(k) 2 ] 2

[0083] c s,41 = cum[s(k),s(k),s(k),s * (k)] = E[s(k) 3 s * (k)]-3E[s(k) 2 ]E[s(k)s * (k)]

[0084] c s,42 = cum[s(k),s(k),s(k),s * (k)]

[0085] =E[s(k) 2 s * (k) 2 ]-E[s(k) 2 ] 2 -2E(s(k)s * (k)] 2

[0086] Where cum[·] represents the cumulative amount calculation expression, s * (k) represents the conjugate of the sequence, and E[·] represents the mathematical expectation. The theoretical values ​​of the high-order cumulants of each modulation method are shown in Table 2.

[0087] Table 2 Theoretical values ​​of high-order cumulants

[0088]

[0089] The 4th-order cumulant eigenvector is constructed as follows:

[0090] f s,1 =|c s,41 | / |c s,42 |

[0091] f s,2 =|c s,40 | / |c s,42 |

[0092] For signals of different modulation orders, the fourth-order cumulant classification feature vector has the following form:

[0093]

[0094] When selecting a classifier, use the Euclidean distance classifier, and the decision criteria are:

[0095]

[0096] In the formula, represents the classification feature vector extracted from the received signal sequence, Represents the distance between the received signal classification feature vector and the feature vector of a certain modulation method in the type set.

[0097] The signal modulation recognition method based on the fourth-order cumulant is as follows Figure 2 shown.

[0098] The above identification method process is applied to cluster signal recognition.

[0099] TETRA is a digital trunked mobile communication system that uses time division multiple access (TDMA) access, 4 time slots per carrier frequency, and QPSK modulation. The signal sequence is sent into the identification method flow, and the modulation mode is successfully identified as QPSK. Further demodulation is attempted to find the TETRA synchronization code.

[0100] (11111111000000000000000000000000000000000000000000000000000000000000000000000000011111111) confirms that the system is TETRA. Figure 3 shown.

[0101] This method first analyzes the anti-fading performance based on high-order cumulant characteristics, and concludes that the ability to suppress multipath interference becomes stronger with the increase of high-order cumulant. Then, taking the cluster signal recognition method based on 4th-order cumulant as an example, it is analyzed and determined that this method not only improves the recognition rate of cluster signals, but also effectively overcomes the influence of multipath channels on classification performance.

[0102] This embodiment further provides a cluster signal recognition and processing system based on high-order cumulant features, including a recognition and processing module, wherein the recognition and processing module executes the cluster signal recognition and processing method based on high-order cumulant features.

[0103] This embodiment also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the cluster signal recognition and processing method based on high-order cumulative quantity features is implemented.

[0104] Ginseng Figure 4 As shown, this embodiment also provides an electronic device, including:

[0105] A memory 201 and a processor 202, wherein the memory 201 and the processor 202 are connected to each other for communication, the memory 201 stores computer instructions, and the processor 202 executes the computer instructions to execute the cluster signal recognition and processing method based on high-order cumulative quantity features.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A cluster signal recognition and processing method based on high-order cumulant features, characterized in that: The steps include: Step 1, analyzing the anti-fading performance of the high-order cumulative amount characteristic to obtain the correlation characteristics of the high-order cumulative amount and the ability of multipath interference; wherein the correlation characteristics are: as the high-order cumulative amount increases, the ability to suppress multipath interference is stronger; Step 2, receiving the signal sequence, calculating the fourth-order cumulant of the signal, constructing a fourth-order cumulant feature vector based on the fourth-order cumulant, extracting the classification feature vector from the received signal sequence, using the Euclidean distance classifier for judgment, and obtaining a signal recognition result.

2. The cluster signal recognition and processing method based on high-order cumulant features according to claim 1 is characterized in that: The step 1 comprises: Assume that the P-order cumulant of the signal sequence s(k) is expressed as c s,pq (l1,l1,…,l p ), where l1,l1,…,l p is the delay value of each sequence, q (q≤p / 2) represents the number of conjugated sequences; because the code element sequences are independent and identically distributed, their high-order cumulants are only in l1,l1,…,l p =0, it has a non-zero value, and we get: In the formula, c r,pq represents the P-order cumulant of the received signal r(k), l1,l1,…,l p =0, q is the number of sequences to be conjugated, and the channel effect is removed by dividing the two cumulative values. Let: f is used as the modulation classification characteristic value, which is called the cumulative characteristic. When there is only one path, that is, when the multipath number L+1 is 1, it is obtained: The influence of the channel is eliminated by taking the modulus of the numerator and denominator respectively, and the eigenvalues ​​of f after passing through the channel and not passing through the channel are equal; in the multipath channel environment, the number of sequences taken conjugate in the cumulative expression is different, and multiple complex numbers are no longer equal after taking the modulus, and the multipath channel affects the eigenvalue f; assuming that h0 is the channel response value of the maximum modulus, when satisfying When , the multipath effect is ignored; Assume there exists a constant R, After transformation: Among them, h max / h0 is the amplitude attenuation coefficient of the multipath channel; According to the ratio of the sub-maximum diameter to the maximum diameter in the 4th, 6th, and 8th order cumulants when R is 5 and 10 respectively, it is found that the ability to suppress multipath interference is greatly improved as the order of the high-order cumulants increases.

3. The cluster signal recognition and processing method based on high-order cumulant features according to claim 2 is characterized in that: The step 2 comprises: The calculation formula of the fourth-order cumulant of the signal, moment-cumulant formula: In the formula, represents the summation within all partitions of I; For a zero-mean complex random sequence, its fourth-order cumulant is expressed as follows: C s,40 =cum[s(k),s(k),s(k),s(k)]=E[s(k) 4 ]-3E[s(k) 2 ] 2 C s，41 =Cum[s(k),s(k),s(k),s*(k)]=E[s(k) 3 s*(k)]-3E[s(k) 2 ]E[s(k)s*(k)] C s，42 =cum[s(k),s(k),s(k),s*(k)] =E[s(k) 2 s*(k) 2 ]-E[s(k) 2 ] 2 -2E[s(k)s*(k)] 2 Where cum[·] represents the cumulative amount calculation expression, s * (k) represents the conjugation of the sequence, and E[·] represents the mathematical expectation; Obtain theoretical values ​​of high-order cumulative quantities for each modulation mode; The 4th-order cumulant eigenvector is constructed as follows: f s，1 =|c s,41 | / |c s,42 | f s，2 =|c s,40 | / |c s,42 | For signals of different modulation orders, the fourth-order cumulant classification feature vector has the following form: When selecting a classifier, use the Euclidean distance classifier, and the decision criteria are: In the formula, represents the classification feature vector extracted from the received signal sequence, Represents the distance between the received signal classification feature vector and the feature vector of a certain modulation method in the type set; The trunking signal sequence is fed into the recognition process to identify the modulation mode, and further demodulate to find the TETRA synchronization code.

4. A cluster signal recognition and processing system based on high-order cumulative quantity features, characterized in that: It comprises an identification processing module, which executes the cluster signal identification processing method based on high-order cumulative quantity features as described in any one of claims 1-3.

5. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, a cluster signal recognition and processing method based on high-order cumulative quantity features as described in any one of claims 1 to 3 is implemented.

6. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the cluster signal recognition and processing method based on high-order cumulative quantity features as described in any one of claims 1 to 3 by executing the computer instructions.

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