A PCMA signal recognition method based on joint probability density distribution

Abstract

The present invention discloses a PCMA signal recognition method based on joint probability density distribution, which belongs to the technical field of communication signal spectrum sensing. The present invention includes the steps of feature parameter selection, pre-derivation of probability density distribution, feature parameter estimation, joint probability calculation and recognition result. The present invention abandons the traditional tree-like branching structure, and instead estimates the joint probability of multiple feature 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 estimated deviations of multiple feature parameters exceed the threshold is less than the probability that the estimated deviation of a single feature parameter exceeds the threshold. Therefore, the present invention improves the estimation accuracy. The method of the present invention is concise, has a moderate amount of calculation, is easy to implement in engineering, and is an important improvement to the existing technology.

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 operate on the same frequency for both communicating parties. Compared to traditional FDMA signals, this reduces bandwidth by half and offers inherent resistance to interception. Reliable identification of PCMA signals is currently a key area of ​​research in spectrum sensing technology for communication signals.

[0003] Traditional PCMA signal recognition methods often employ a tree-like branching structure. Specifically, one or more characteristic parameters are selected and compared with a threshold value to select different branches for signal recognition. However, in actual communication systems, the power ratio of the signals between the communicating parties varies within a certain range due to factors such as distance and weather. These factors also cause variations in the received signal-to-noise ratio (SNR) of the detecting party, ultimately manifesting as a range of characteristic parameter values. When the estimated deviation of a parameter exceeds the decision threshold, an incorrect decision branch is selected. The more root-level the branch error, the wider the impact on subsequent decisions. Therefore, existing PCMA signal recognition technology still has room for improvement. Summary of the Invention

[0004] The present invention aims to overcome the shortcomings of the above-mentioned prior art and proposes a PCMA signal recognition method based on joint probability density distribution. This method abandons the traditional tree-like branching 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 the decision.

[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 includes the following steps:

[0007] (1) Feature parameter selection: Power spectrum, second-order / fourth-order / 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 statistically analyzed 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: pre-process the sampled data after A / D conversion to obtain zero-frequency baseband data, and calculate 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 parameter under the modulation type to obtain the probability value under different characteristic parameters. 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] Furthermore, in step (3), if the zero-frequency baseband data is:

[0014] x k ,k=0,1,2,…

[0015] Where 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] Where x * Indicates taking the conjugate of x; p represents the total order, and q represents the order after taking the conjugate of the data, 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 results, M 21 When p=2 and q=1, M pq The calculation results of

[0026] The fourth-order cumulant is calculated as:

[0027]

[0028] Where M 40 When p=4 and q=0, M pq The calculation results, 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 results, M 41 When p=4 and q=1, M pq The calculation results of .

[0032] Furthermore, in step (4), the estimated values ​​of each characteristic parameter of the zero-frequency baseband data are respectively substituted into the probability density distribution of the corresponding characteristic parameter 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-like branching structure and estimates the joint probability density of multiple characteristic parameters under different types of PCMA signals, avoiding the problem 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 simple, has a moderate amount of calculation, and is easy to implement in engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 4 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] Figures 3 to 6 Schematic diagram of the probability density of four characteristic parameters: power spectrum, second-order cumulant, fourth-order cumulant, and sixth-order cumulant in an embodiment of the present invention. DETAILED DESCRIPTION

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

[0040] A PCMA signal recognition method based on joint probability density distribution is proposed. The method calculates the estimated values ​​of multiple characteristic parameters, pre-derives the probability density distribution of the 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 includes 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) Pre-derive the probability density distribution: 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 of PCMA signals taking different characteristic parameter values ​​under each modulation mode as uniformly distributed. Then, the probability density distribution of the four characteristic parameters under each modulation mode can be obtained, as follows: Figures 3 to 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 this zero-frequency baseband data:

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

[0046] x k ,k=0,1,2,…

[0047] Where 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] Where x * Indicates taking the conjugate of x; p represents the total order, and q represents the order after taking the conjugate of the data, 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 results, M 20 When p=2 and q=1, M pq The calculation results of

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

[0059]

[0060] Where M 40 When p=4 and q=0, M pq The calculation results, 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 results, M 41 When p=4 and q=1, M pq The calculation results of .

[0064] (4) Joint Probability Calculation: Substitute the four estimated values ​​obtained in step (3) into the four probability density distributions under each modulation mode, respectively, to 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 searched 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, resulting in three joint probability values.

[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 the signal identification is completed.

[0066] The present invention abandons the traditional tree-like branching structure and instead estimates the joint probability of multiple characteristic parameters under different PCMA signal types, selecting the one with the highest probability as the PCMA type identification result. From a probabilistic perspective, the probability of multiple characteristic parameter estimation deviations exceeding the threshold is lower than the probability of a single characteristic parameter estimation deviation exceeding the threshold, thus improving estimation accuracy. The present invention offers advanced technology, a simple method, moderate computational effort, and ease of engineering implementation, representing a significant improvement over existing technologies.

[0067] It should be understood that the above description of the specific implementation methods of this patent is merely an exemplary description listed to facilitate ordinary technicians in this field to understand the patent solution, 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 solution 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: The power spectrum and the second-order, fourth-order, and sixth-order cumulants are selected as the four 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 statistically analyzed to obtain the value range of the four characteristic parameters of the PCMA signal under each modulation type, and obtain the probability density distribution of the four characteristic parameters of the PCMA signal under each modulation type; (3) Characteristic parameter estimation: pre-processing the sampled data after A / D conversion to obtain zero-frequency baseband data, and calculating the estimated values ​​of the four characteristic parameters of the zero-frequency baseband data; (4) Joint probability calculation: For each modulation type, the estimated values ​​of the four characteristic parameters of the zero-frequency baseband data are substituted into the probability density distribution of the four characteristic parameters corresponding to the modulation type to obtain the probability values ​​under the four characteristic parameters. Then, the joint probability value under the modulation type is calculated based on the probability values ​​under the four 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: Where, Indicates data index; The power spectrum is calculated as: Where, Express Taking the average, 2, 4, and 8 correspond to BPSK, QPSK, and 8PSK modulation modes respectively; The higher-order moments are calculated as: Where, Express Take conjugate; represents the total order, Indicates the order of the data after conjugation, which are all non-negative integers; The second-order cumulants are calculated as: Where, for 、 hour The calculation results, M 21 for 、 hour The calculation results of The fourth-order cumulant is calculated as: Where, for 、 hour The calculation results are: for 、 hour The calculation results of The calculation method of the sixth-order cumulant is: Where, for 、 hour The calculation results are: for 、 hour The calculation results 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 the four characteristic parameters of the zero-frequency baseband data are respectively substituted into the probability density distribution of the four characteristic parameters corresponding to the modulation type to obtain the probability values ​​under the four 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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