PCMA signal identification method based on joint probability density distribution

Abstract

The invention discloses a PCMA signal identification method based on joint probability density distribution, and belongs to the technical field of communication signal spectrum sensing. The method comprises the steps of feature parameter selection, probability density distribution pre-derivation, feature parameter estimation, joint probability calculation, result recognition and the like. According to the method, a traditional tree branch structure is abandoned, the joint probability of a plurality of characteristic parameters under different types of PCMA signals is estimated, and the characteristic parameter with the maximum probability is selected as a PCMA type identification result. From the perspective of probability, the probability that the estimation deviation of a plurality of characteristic parameters exceeds the threshold is smaller than the probability that the estimation deviation of a single characteristic parameter exceeds the threshold, so that the estimation accuracy is improved. The method is simple and clear, moderate in calculation amount and easy for engineering realization, and is an important improvement on the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication signal spectrum sensing, and specifically refers to a PCMA signal recognition method based on joint probability density distribution. Background Art

[0002] PCMA (Paired Carrier Multiple Access) satellite communication signals, both communicating parties work at the same operating frequency. Compared with traditional FDMA signals, it can save half of the bandwidth resources and has natural anti-interception performance. At present, reliable identification of PCMA signals is an important direction in communication signal spectrum sensing technology.

[0003] Traditional PCMA signal recognition methods mostly use a tree-like branch structure, that is, one or more characteristic parameters are selected, and different branches are selected by comparison with the threshold value to achieve signal recognition. However, in actual communication systems, due to the influence of factors such as distance and weather, the power ratio of the signals of the two communicating parties varies within a certain range, and these factors will also cause changes in the received signal-to-noise ratio of the reconnaissance party, which ultimately manifests as the value of the characteristic parameter varying within a certain range. When the estimated deviation of a parameter exceeds the decision threshold, an incorrect decision branch will be selected, and the more root branch errors there are, the wider the impact on subsequent decisions. It can be seen that there is still room for improvement in the PCMA signal recognition technology in the prior art. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects in the above-mentioned prior art and propose a PCMA signal recognition method based on joint probability density distribution. The method abandons the traditional tree branch structure and uses multiple characteristic parameters to improve the reliability of key decision branches. In addition, for characteristic parameters with small allowable deviations, other characteristic parameters are used to assist in making decisions.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A PCMA signal recognition method based on joint probability density distribution comprises the following steps:

[0007] (1) Feature parameter selection: Power spectrum, second-order, fourth-order, and sixth-order cumulants are selected as feature parameters for PCMA signal recognition;

[0008] (2) Pre-derive the probability density distribution: Under each modulation type, the characteristic parameters of the PCMA signal with different power, frequency, phase, and timing parameters are counted to obtain the value range of each characteristic parameter of the PCMA signal under each modulation type, and obtain the probability density distribution of each characteristic parameter of the PCMA signal under each modulation type;

[0009] (3) Characteristic parameter estimation: preprocessing the sampled data after A / D conversion to obtain zero-frequency baseband data, and calculating the estimated values ​​of each characteristic parameter of the zero-frequency baseband data;

[0010] (4) Joint probability calculation: For each modulation type, the estimated values ​​of each characteristic parameter of the zero-frequency baseband data are substituted into the probability density distribution of the corresponding characteristic parameters under the modulation type to obtain the probability values ​​under different characteristic parameters, and then the joint probability value under the modulation type is calculated based on the probability values ​​under different characteristic parameters;

[0011] (5) Recognition result: The modulation type corresponding to the maximum joint probability value is selected as the recognition result to complete the signal recognition.

[0012] Furthermore, the modulation types in step (2) include BPSK, QPSK, and 8PSK.

[0013] Further, in step (3), if the zero-frequency baseband data is:

[0014] x k ,k=0,1,2,…

[0015] In the formula, k represents the data index;

[0016] The power spectrum is calculated as:

[0017]

[0018] Where, E{x} represents the average of x, and 2 / 4 / 8 correspond to BPSK, QPSK, and 8PSK modulation modes respectively;

[0019] The higher-order moments are calculated as:

[0020]

[0021] In the formula, x * represents the conjugate of x; p represents the total order, and q represents the order of the data after conjugation, both of which are non-negative integers;

[0022] The second-order cumulants are calculated as:

[0023] C 20 =M 20

[0024] C 21 =M 21

[0025] Where M 20 When p=2 and q=0, M pq The calculation result, M 21 When p=2, q=1, M pq The calculation results of

[0026] The calculation method of the fourth-order cumulant is:

[0027]

[0028] Where M 40 When p=4 and q=0, M pq The calculation result, M 42 When p=4 and q=2, M pq The calculation results of

[0029] The calculation method of the sixth-order cumulant is:

[0030]

[0031] Where M 63 When p=6 and q=3, M pq The calculation result, M 41 When p=4 and q=1, M pq The calculation result of .

[0032] Furthermore, in step (4), the estimated values ​​of each characteristic parameter of the zero-frequency baseband data are substituted into the probability density distribution of the corresponding characteristic parameters under the modulation type to obtain the probability values ​​under different characteristic parameters. The specific method is: divide the probability density distribution into intervals, directly find the interval where the estimated value is located, integrate the probability density of the interval, and obtain the corresponding probability value.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] 1. The present invention abandons the traditional tree branch structure and estimates the joint probability density of multiple characteristic parameters under different types of PCMA signals, avoiding the deficiency that the estimation deviation of a single characteristic parameter exceeds the threshold and causes all subsequent branch decisions to be wrong, thereby improving the estimation accuracy.

[0035] 2. The method of the present invention is concise, has a moderate amount of calculation, and is easy to implement in engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a PCMA signal recognition method based on joint probability density distribution in an embodiment of the present invention.

[0037] Figure 2 Schematic diagram of the calculation of the joint probability of characteristic parameters in an embodiment of the present invention.

[0038] Figure 3 to Figure 6 It is a schematic diagram of the probability density of four characteristic parameters, namely, the power spectrum, the second-order cumulant, the fourth-order cumulant, and the sixth-order cumulant in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0040] A PCMA signal recognition method based on joint probability density distribution, the method calculates the estimated values ​​of multiple characteristic parameters, pre-derives the probability density distribution of characteristic parameters of different types of PCMA signals, finds the probability of each estimated value under different types of PCMA signals, obtains the joint probability of the signal estimated value under different types of PCMA signals, and selects the PCMA signal with the maximum joint probability as the recognition result.

[0041] like Figure 1 and Figure 2 As shown, the method specifically comprises the following steps:

[0042] (1) Feature parameter selection: PCMA signals usually use modulation methods such as PSK / QAM. Statistical values ​​such as power spectrum, second-order / fourth-order / sixth-order cumulants, etc. can be selected as feature parameters for signal identification. There are four feature parameters in total.

[0043] (2) Derivation of probability density distribution in advance: Consider three modulation modes, namely BPSK, QPSK, and 8PSK. For each modulation mode, collect PCMA signals with different signal parameters such as power, frequency, phase, and timing. Calculate the four characteristic parameters of these PCMA signals and obtain the value range of each characteristic parameter. Consider the events in which the PCMA signal takes different characteristic parameter values ​​under each modulation mode as uniform distribution. Then, the probability density distribution of the four characteristic parameters under each modulation mode can be obtained, as follows: Figure 3 to Figure 6 shown.

[0044] (3) Estimation of characteristic parameters: Obtain the PCMA signal to be identified, perform preprocessing such as frequency conversion and filtering on the sampled data after A / D conversion to obtain zero-frequency baseband data, and then calculate the estimated values ​​of the four characteristic parameters for the zero-frequency baseband data:

[0045] Assume that the zero-frequency baseband data is:

[0046] x k ,k=0,1,2,…

[0047] In the formula, k represents the data index;

[0048] The power spectrum calculation method is:

[0049]

[0050] Where, E{x} represents the average of x, and 2 / 4 / 8 correspond to BPSK, QPSK, and 8PSK modulation modes respectively;

[0051] The higher-order moments are defined as:

[0052]

[0053] In the formula, x * represents the conjugate of x; p represents the total order, and q represents the order of the data after conjugation, both of which are non-negative integers;

[0054] The second-order cumulant calculation method is:

[0055] C 20 =M 20

[0056] C 21 =M 21

[0057] Where M 20 When p=2 and q=0, M pq The calculation result, M 20 When p=2, q=1, M pq The calculation results of

[0058] The fourth-order cumulant calculation method is:

[0059]

[0060] Where M 40 When p=4 and q=0, M pq The calculation result, M 42 When p=4 and q=2, M pq The calculation results of

[0061] The calculation method of the sixth-order cumulant is:

[0062]

[0063] Where M 63 When p=6 and q=3, M pq The calculation result, M 41 When p=4 and q=1, M pq The calculation result of .

[0064] (4) Joint probability calculation: Substitute the four estimated values ​​calculated in step (3) into the four probability density distributions under each modulation mode, and obtain four probability values ​​under each modulation mode. To facilitate calculation, the probability density distribution can be divided into intervals, and the interval where the estimated value is located can be directly found and the probability value can be obtained by integration. Then, the joint probability is calculated based on the four probability values ​​under each modulation mode, and three joint probability values ​​are obtained.

[0065] (5) Identification result: The modulation type corresponding to the maximum joint probability value is selected as the modulation type of the PCMA signal to be identified, and signal identification is completed.

[0066] The present invention abandons the traditional tree-like branch structure, but estimates the joint probability of multiple characteristic parameters under different types of PCMA signals, and selects the one with the largest probability as the PCMA type recognition result. From the perspective of probability, the probability that the estimation deviations of multiple characteristic parameters all exceed the threshold is less than the probability that the estimation deviation of a single characteristic parameter exceeds the threshold, so the method of the present invention improves the estimation accuracy. The present invention has advanced technology, concise method, moderate calculation amount, and is easy to implement in engineering, which is an important improvement on the prior art.

[0067] It should be understood that the above description of the specific implementation methods of this patent is only an exemplary description listed to facilitate ordinary technicians in the field to understand the patent scheme, and does not imply that the scope of protection of this patent is limited to these individual examples. Ordinary technicians in this field can fully understand the technical scheme of this patent and, without any creative work, obtain more specific implementation methods by combining technical features, replacing some technical features, adding more technical features, etc. to the examples listed in this patent. All these specific implementation methods are within the scope of the claims of this patent. Therefore, these new specific implementation methods should also be within the scope of protection of this patent.

Claims

1. A PCMA signal recognition method based on joint probability density distribution, characterized in that: The following steps are involved: (1) Feature parameter selection: Power spectrum, second-order, fourth-order, and sixth-order cumulants are selected as feature parameters for PCMA signal recognition; (2) Pre-derive the probability density distribution: Under each modulation type, the characteristic parameters of the PCMA signal with different power, frequency, phase, and timing parameters are counted to obtain the value range of each characteristic parameter of the PCMA signal under each modulation type, and obtain the probability density distribution of each characteristic parameter of the PCMA signal under each modulation type; (3) Characteristic parameter estimation: preprocessing the sampled data after A / D conversion to obtain zero-frequency baseband data, and calculating the estimated values ​​of each characteristic parameter of the zero-frequency baseband data; (4) Joint probability calculation: For each modulation type, the estimated values ​​of each characteristic parameter of the zero-frequency baseband data are substituted into the probability density distribution of the corresponding characteristic parameters under the modulation type to obtain the probability values ​​under different characteristic parameters, and then the joint probability value under the modulation type is calculated based on the probability values ​​under different characteristic parameters; (5) Recognition result: The modulation type corresponding to the maximum joint probability value is selected as the recognition result to complete the signal recognition.

2. A PCMA signal recognition method based on joint probability density distribution according to claim 1, characterized in that: The modulation types in step (2) include BPSK, QPSK, and 8PSK.

3. A PCMA signal recognition method based on joint probability density distribution according to claim 1, characterized in that: In step (3), if the zero-frequency baseband data is: x k ,k=0,1,2,… In the formula, k represents the data index; The power spectrum is calculated as: Where, E{x} represents the average of x, and 2 / 4 / 8 correspond to BPSK, QPSK, and 8PSK modulation modes respectively; The higher-order moments are calculated as: In the formula, x * represents the conjugate of x; p represents the total order, and q represents the order of the data after conjugation, both of which are non-negative integers; The second-order cumulants are calculated as: C 20 =M 20 C 21 =M 21 Where M 20 When p=2 and q=0, M pq The calculation result, M 21 When p=2, q=1, M pq The calculation results of The calculation method of the fourth-order cumulant is: Where M 40 When p=4 and q=0, M pq The calculation result, M 42 When p=4 and q=2, M pq The calculation results of The calculation method of the sixth-order cumulant is: Where M 63 When p=6 and q=3, M pq The calculation result, M 41 When p=4 and q=1, M pq The calculation result of .

4. A PCMA signal recognition method based on joint probability density distribution according to claim 1, characterized in that: In step (4), the estimated values ​​of each characteristic parameter of the zero-frequency baseband data are substituted into the probability density distribution of the corresponding characteristic parameters under the modulation type to obtain the probability values ​​under different characteristic parameters. The specific method is: divide the probability density distribution into intervals, directly find the interval where the estimated value is located, integrate the probability density of the interval, and obtain the corresponding probability value.

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