A TCN / ATC integrated signal recognition method

Abstract

This invention belongs to the field of signal processing technology, and specifically relates to an integrated TCN and ATC signal identification method. Step 1: Bandpass pre-filtering is performed on the received radio frequency signal, and the pre-filtered data is extracted into two channels. Step 2: DFT processing and spectrum analysis are performed on channel A data to obtain the signal's spectrum distribution map; frequency domain sorting and filtering are performed on the TCN and ATC distributed signals; delay processing is performed on channel B data. Step 3: Based on the information from the spectrum distribution map obtained from channel A, digital bandpass filtering is performed on channel B data. Step 4: Coherent demodulation is performed to obtain I-channel and Q-channel signals. Step 5: The I-channel and Q-channel signals are delayed respectively to obtain I'-channel and Q'-channel signals. Step 6: The I'-channel and Q'-channel signals are superimposed to obtain the Σ signal. Step 7: Pulse matched filtering is performed on the Σ signal to identify the signal type; if it is an ATC signal, proceed to step 8; if it is a TCN signal, proceed to step 9. Step 8: Pattern recognition is performed on the ATC signal. Step 9: TCN signal is identified and decoded.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, and specifically relates to an integrated TCN and ATC signal recognition method. Background Technology

[0002] With the rapid development of electronic information technology, traditional airborne discrete equipment is gradually evolving towards functional integration. In the military field, in order to enhance combat capabilities, combat platforms are often developing towards miniaturization and diversification, striving to equip a variety of electronic information devices on the smallest possible platform. This leads to increased electromagnetic interference between different systems, and functional integration is an effective way to reduce inter-system interference.

[0003] Given that airborne TACAN and ATC currently employ separate functional implementations, a review of their operating frequency bands, implementation methods, and processing logic reveals that traditional radio frequency physical integration can be further integrated through unified functional algorithms. This allows for greater utilization of multi-dimensional resource information, including physical, spatial, and electromagnetic spectrum data, on top of traditional radio frequency integration, thereby enhancing combat performance indicators. Summary of the Invention

[0004] The purpose of this invention is to propose an integrated design method for TACAN and ATC signal recognition, specifically referring to the integrated processing of traditional TACAN and ATC signal detection and recognition through direct radio frequency acquisition, and the sorting and recognition of TACAN and ATC signals by using a set of waveform algorithms.

[0005] The technical solution of this invention:

[0006] A method for integrated TCN and ATC signal recognition, comprising the following steps:

[0007] Step 1: Perform bandpass pre-filtering on the received RF signal, and extract the pre-filtered data into two channels;

[0008] Step 2: Perform DFT processing on data A to obtain a complex domain spectrum, calculate the spectrum data, perform spectrum analysis based on the spectrum data to obtain the signal spectrum distribution, perform frequency domain sorting and filtering on the signals with TCN and ATC distributions to obtain the signals within the TCN and ATC bandwidths; perform delay processing on data B.

[0009] Step 3: Based on the spectrum distribution information obtained from channel A, perform digital bandpass filtering on the data from channel B;

[0010] Step 4: Perform coherent demodulation to obtain the I-channel signal and the Q-channel signal;

[0011] Step 5: Delay the I-channel signal and the Q-channel signal respectively to obtain the I'-channel signal and the Q'-channel signal;

[0012] Step 6: Superimpose the I' and Q' signals to obtain the Σ signal;

[0013] Step 7: Perform pulse matched filtering on the Σ signal to identify the signal type. If it is an ATC signal, proceed to Step 8; if it is a TCN signal, proceed to Step 9.

[0014] Step 8: Perform pattern recognition on the ATC signal;

[0015] Step 9: Identify and decode the TCN signal.

[0016] Furthermore, in step two, the DFT processing procedure is as follows:

[0017]

[0018] in, x(n) is the input digital signal.

[0019] X(k) is used to calculate the peak value at each frequency point, which can effectively determine the position of the current frequency point.

[0020] Furthermore, in step three, the digital bandpass filter uses an FIR filter, the basic function of which is:

[0021]

[0022] Where x(n) is the input signal, h(n) is the matched filter template, and y(n) is the filtered spectrum data.

[0023] Furthermore, in step five, the signal delay process involves selecting an optimal N-beat delay based on empirical parameters; N is no greater than the shortest length of an ATC or TCN pulse data.

[0024] Furthermore, in step six, the formula for calculating the Σ signal is as follows:

[0025] Σ=I'(n)+Q'(n).

[0026] Furthermore, in step seven, the formula for calculating the pulse matched filter is as follows:

[0027]

[0028] Where N is the length of the digital domain of the signal, which is calculated based on the template of each signal;

[0029] p(n)=∑P1(n-T1)+P2(n-T2)+P3(n-T3)+P5(n-T5)

[0030] Where T1, T2, T3, and T5 represent the positions of the signal pulses, respectively.

[0031] Furthermore, in step eight, the signal interrogation mode is identified based on the ATC signal template, as follows:

[0032] The input signal pulse sequence is s(n), the template for the A mode query signal is ATC_A(n), the template for the C mode query signal is ATC_C(n), the template for the A / C mode query signal is ATC_A / C(n), and the template for the S mode query signal is ATC_S(n).

[0033] The input signal pulse sequence is subjected to sliding interpolation calculation using different templates to obtain the Δ signal, as shown in the following formula:

[0034] Δ=∑s(Tn)-ATC_X(n)

[0035] ATC_X(n) is ATC_A(n), ATC_C(n), ATC_A / C(n), or ATC_S(n);

[0036] Determine whether the Δ signal is similar to the query signal of the corresponding template. If they are similar, then it is the query signal of the pattern corresponding to the template.

[0037] Furthermore, the TCN signal identification and decoding process is as follows:

[0038] A Gaussian pulse template based on TACAN signals is used for identification; the judgment is made through pulse decoding.

[0039] Pulse decoding performs subtraction on the received signal and accumulates the pulses to obtain relevant information; the pulse decoding signal simultaneously yields the ranging pulse and the reference pulse.

[0040] Pulse decoding pair

[0041]

[0042] p(n) is the matched pulse signal, N is the cumulative number of pulses, N = 12. By judging the Ω signal, the following conclusions can be drawn:

[0043] Ω<Ω0: The signal is the amplitude envelope signal; Ω>Ω1: The signal reference pulse and ranging echo signal, where Ω1>Ω0, and the values ​​of the two are based on empirical values ​​from the database.

[0044] The signal is matched to the template by pulse decoding to obtain an envelope signal composed of several pulse pairs. The presence of the TACAN signal is determined by detecting the spectrum of the envelope of the received pulse stream.

[0045] The signal amplitudes are compared within one pulse width after the pulse arrival time, and the maximum amplitude is taken as the peak point of the pulse. These peak points form the envelope curve.

[0046] The signal pulse after peak detection is a composite signal composed of 135Hz and 35Hz. The composite signal can be demodulated by coherent signals of 135Hz and 35Hz respectively to obtain the envelope pulse signals of the two signals, and then the phase signal.

[0047] The signal obtained by mutual demodulation has multi-spectral components. Filtering the multi-spectral components yields the cosine signal waveform of the signal.

[0048] After normalizing the amplitude, x(n) > S, x(n) = s0

[0049] For a pulse pair, the rule for x(n) to identify the master reference pulse is:

[0050] In X mode, the main reference pulse group consists of 24 pulses, which are paired into a pulse pair, with a total of 12 pulse pairs. The interval between the two pulses in each pulse pair is 12±0.1μs, and the interval between any two adjacent pulse pairs is 30±0.1μs.

[0051] In Y mode, the main reference pulse group consists of 13 pulses with an interval of 30±0.1μs between adjacent pulses;

[0052] Using this pulse matching criterion, the main reference pulse and its mode are determined, and the pulse time of the main reference pulse is obtained as t. 主 ;

[0053] For a pulse pair, the rule for x(n) to identify the auxiliary reference pulse is:

[0054] In X mode, the auxiliary reference pulse group consists of 12 pulses, which are paired up in pairs, just like the main reference, for a total of 6 pulse pairs. The interval between the two pulses in each pulse pair is 12±0.1μs, and the interval between any two adjacent pulse pairs is 24±0.1μs.

[0055] In Y mode, the auxiliary reference pulse group also consists of 13 pulses, but the interval between adjacent pulses is 15±0.1μs;

[0056] Using this pulse matching criterion, the auxiliary reference pulse and its mode can be determined; and the pulse time of the main reference pulse can be obtained as t. 辅 ;

[0057] For a pulse pair, the rule for x(n) to identify the ranging pulse is:

[0058] The distance response pulse consists of two pulses, with a pulse interval of 12±0.1μs in X mode and 30±0.1μs in Y mode. When the ground base station receives an interrogation signal from the airborne equipment, it will transmit a response pulse after a specified delay; this delay is 50±0.1μs in X mode and 56±0.1μs in Y mode. During the delay, the ground base station does not transmit other signals, but since the distance response pulse has a priority only higher than the padding pulse, it will transmit other types of pulses first before calculating the response delay; thus, the arrival time t of the distance response pulse is calculated. 测距 ;

[0059] For a pulse pair, x(n) identifies the pulse according to the following rules:

[0060] The identification pulse consists of two pulses, with a pulse interval of 12±0.1μs in X mode and 30±0.1μs in Y mode. The identification pulse is synchronized with the azimuth reference pulse group, and there should be a balanced pulse with the same code as the identification pulse 100±10μs after it. The identification signal is obtained by sampling the identification pulse for several seconds.

[0061] The coarse azimuth measurement is performed using the main reference pulse time t in S10.2.1.2.1. 主 The information of the 0-phase of the azimuth coarse pulse is used to obtain the corresponding time difference:

[0062] Δt 主 =min{t 主 -t n},(t 主 -t n )>0

[0063] The relative time Δt was calculated. 主 The corresponding azimuth values ​​are obtained through conversion:

[0064]

[0065] By using the auxiliary quasi-pulse time t 主 The information of the 0-phase of the azimuth coarse pulse is used to obtain the corresponding time difference:

[0066] Δt 辅 =min{t 辅 -t n},(t 辅 -t n )>0

[0067] relative time Δt 辅 The corresponding azimuth values ​​are obtained through conversion:

[0068]

[0069] By integrating the received TACAN and ATC signals, and based on prior knowledge and template data, the signal type can be effectively identified, and the internal feature distribution of the signal can be determined. This integrated identification and processing effectively addresses the shortcomings of traditional methods that identify each waveform separately.

[0070] The beneficial effects of this invention are:

[0071] This invention can effectively enhance the integrated application capabilities of TACAN and ATC signals in the military field, and effectively identify navigation and identification signals in increasingly complex electromagnetic environments, thereby enhancing the platform's adaptability and improving its overall combat effectiveness. Attached Figure Description

[0072] Figure 1 : Overall processing flowchart of this invention;

[0073] Figure 2 : Flowchart of spectrum calculation;

[0074] Figure 3 Frequency pre-sorting information diagram;

[0075] Figure 4 : FIR filter;

[0076] Figure 5 : MATLAB FDATool tool;

[0077] Figure 6 Schematic diagram of coherent demodulation;

[0078] Figure 7 A and C query signal format;

[0079] Figure 8 S-mode query signal format;

[0080] Figure 9 : TACAN pulse pair signal format;

[0081] Figure 10 : Schematic diagram of the waveform after peak detection. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0083] This invention relates to a design method for integrated TACAN and ATC signal recognition, specifically referring to the integrated processing of traditional TACAN and ATC signal detection and recognition through direct radio frequency acquisition, and the sorting and recognition of TACAN and ATC signals by using a waveform algorithm.

[0084] The specific implementation process of this invention is as follows: Figure 1 As shown.

[0085] S1. Broadband RF signal decomposition based on direct RF sampling

[0086] In this method, the main operating frequency band is 960MHz to 1.3GHz. Therefore, before the sampling signal begins, to ensure rapid access of the effective signal, the RF signal is pre-filtered using a bandpass filter to remove frequencies below 960MHz and above 1.3GHz. The sampling rate is selected considering the signal preservation characteristic of the Nyquist sampling theorem. It is known that the input signal is a signal with a finite continuous bandwidth; therefore, the sampling frequency ω... s >2ω m , where ω s = 2π / T, where T is the sampling period.

[0087] Therefore, in this scheme, the sampling rate is selected as 3.0 Msps.

[0088] After sampling the signal, based on signal processing characteristics, the input fabrication signal is decimated. The decimated signal is then processed in two separate paths. Here, the decimation coefficients do not violate Shannon's sampling theorem. The process proceeds to steps S2 and S1.1 respectively.

[0089] S1.1, Frequency Selective Filter

[0090] A wide-bandgap signal contains all the characteristic information within a bandwidth of 960MHz to 1.3GHz. However, for TCN and ATC interrogation signals, they are characterized by a single frequency point and strong signal correlation. Therefore, the signal can be estimated from the frequency domain by performing DFT processing on the sampled signal.

[0091]

[0092] in, x(n) is the input digital signal.

[0093] X(k) is used to calculate the peak value at each frequency point, which can effectively determine the position of the current frequency point.

[0094] Here, the spectrum of the complex field can be obtained. Through operations on the real and imaginary parts, an estimate of the spectrum data can be obtained. Where X(k) > X sRecord the positions f1, f2, f3, ... of the values ​​of X(k). Here, X s This is prior information. The calculation process is as follows: Figure 2 .

[0095] Therefore, the set of spectral data is: f(n) = {f1, f2, f3...}. f(n) is the set of spectra.

[0096] S1.2, Frequency sorting and filtering

[0097] By estimating the statistical peak value and based on prior information, peak selection is performed on the spectral signal, i.e., f > f. max f max This is prior information. Multiple frequency points can be obtained, and here the count is f(n) = {f1, f2, f3...}. The allocation information is as follows: Figure 3 .

[0098] Here, the spectral information of f'(n) is stored and input to S3 for digital down-conversion and filtering.

[0099] S2, Delay processing

[0100] The first signal is delayed according to the processing cycle, and the delay period is determined by the time delay of processing S1.1 to S1.2.

[0101] S3, Digital Bandpass Filter

[0102] Digital bandpass filtering is performed based on the spectrum in f(n) to pre-sort and filter the signal. According to prior information, the ATC query signal is a point-frequency signal with a frequency of 1030MHz. If f(n) contains this spectrum information, the filter is set to 8MHz. The remaining frequencies can be TCN frequency information, and the filter is set to 3MHz. Here, the intermediate frequency of the filter is set to the center frequency of the signal to be demodulated in f(n), and the frequency-converted signal is x(n).

[0103] Here, the digital bandpass filter uses an FIR filter, and its basic function is:

[0104]

[0105] In the formula, the tap coefficients h(n) of the N filters are multiplied by the N sampled data x(n) respectively, and the sum of the results is the output. The basic structure diagram of the FIR filter implementation is shown below. Figure 4 .

[0106] Here, in this method, the fdatool tool in MATLAB can be used to pre-select N. For example... Figure 5 .

[0107] S4, Coherent Demodulation (Digital Down-Conversion)

[0108] The x(n) signal is converted to baseband frequency using digital down-conversion technology based on the spectrum information of f(n). Here, the digital down-conversion uses a single-stage down-conversion method, mixing the digital signal with two coherent and positive signals, and then decimating the signal using a decimation filter to achieve down-conversion, resulting in the I-channel and Q-channel signals. For example... Figure 6 As shown.

[0109] Right now

[0110] I(n) = x(n)·cos(ωn)

[0111] Q(n) = x(n)·sin(ωn)

[0112] The I-channel and Q-channel signals are complex signals with spectral replication. To ensure effective out-of-band suppression, they can be processed using a low-order low-pass filter. Similarly, an FIR low-pass filter is selected.

[0113] In this way, we obtain a functional signal with a clean spectrum and single information.

[0114] S5.1, S5.2, S6.1, S6.2: Signal delay processing

[0115] To maximize the received signal power spectrum, considering that both ATC and TCN signals are point-frequency signals, the signal power spectrum can be obtained by adding the delays of the I and Q signals. Here, the signal delay phase is π / 4 phase.

[0116] S7, Signal Superposition, Phase Estimation

[0117] The delayed aligned I-channel and Q-channel signals are added together, i.e.:

[0118] Σ=I'(n)+Q'(n)

[0119] Δ=I'(n) / F(n)

[0120] Thus, the envelope and difference signal of the received signal can be obtained from the Σ and Δ signals, respectively. In the method described in this paper, only the Σ signal is used for processing.

[0121] S8, Pulse Matching Filter

[0122] Considering signal correlation, ATC signals have fixed pulse characteristics, while TCN signals have fixed filled pulse characteristics. Since the pulse pattern here is not pulse-shaped, the time-domain characteristics of the pulse signal are not obvious. The method described in this paper can use matched filtering to perform correlation analysis on the pulse signal, thereby identifying whether it is a TCN signal or an ATC signal.

[0123] Matched filtering algorithms in the time domain include:

[0124]

[0125] Where N is the length of the digital domain of the signal, p(n) is the template of the known signal and the matched filter, and T is the delay period. This is calculated based on the template of each signal.

[0126] In this way, two signals with related characteristics can obtain higher energy values, and it can be determined whether they are ATC signals, TCN signals or other signals.

[0127] Regarding the choice of matched filters, ATC and TCN signals have different characteristics.

[0128] The ATC interrogation signal format is as follows: Figure 7 and Figure 8 .

[0129] According to the secondary radar interrogation signal standard, modes A and C are determined by the pulse position information of pulses P1 and P3, where pulse P2 is a sidelobe suppression pulse. Mode S determines the arrival of the signal by the pulse positions of pulses P1 and P2.

[0130] Therefore, the matched filter for the ATC signal can be expressed as:

[0131] p(n)=∑P1(n-T1)+P2(n-T2)+P3(n-T3)+P5(n-T5)

[0132] Where T1, T2, T3, and T5 represent the positions of the signal pulses, respectively.

[0133] According to the TCN standard, the characteristics of TCN signals vary between pulses depending on the mode (X, Y).

[0134] According to the TCN standard requirements, similar to ATC waveforms, TCN signal identification can be achieved through matching calculations of two pulse pairs and correlation peak matching. Here, the TCN signal is divided into X mode and Y mode. It should be noted that the width of the reference pulse signal is the same in both X and Y modes, 3.5 ± 1 μs, but the spacing between pulses differs in different modes. In X mode, the pulse pair spacing of the main reference pulse group is 30 ± 0.1 μs, and the spacing between two pulses within the same pulse pair is 12 ± 0.1 μs. A main reference pulse group contains a total of 12 Gaussian pulse pairs. In Y mode, the main reference pulse group consists of 13 Gaussian single pulses with a pulse spacing of 30 ± 0.1 μs. The pulse group repetition frequency of the main reference pulse group is 15 ± 0.3 Hz.

[0135] S9, Signal Recognition

[0136] By matching and identifying the signal modules in S8, it can be seen that ATC and TCN signals contain 5 modes and 2 modes respectively. That is, by analyzing the sign characteristics of the pulse, the specific signal can be identified.

[0137] If the signals of ATC and TCN cannot be matched, it proves that they are not navigation and surveillance signals within this frequency band. Further differentiation of signal characteristics is needed to determine whether they are communication signals or other signals.

[0138] Through signal recognition, ATC signals and TACAN signals can be identified and information extracted through S10.1 and S10.2 respectively.

[0139] S10.1 Interrogation Signal Extraction and Recognition

[0140] If the identified signal is an ATC signal, the interrogation pattern can be identified based on the ATC signal template. Since interrogation signals often contain multipath reflections, sidelobe suppression via P2 pulses is necessary. Therefore, the interrogation signal identification method can be calculated using the cross-correlation of the template.

[0141] The query signals include A-mode query, C-mode query, A / C-mode query, and S-mode query.

[0142] Here, the input signal pulse sequence is x(n)s(n).

[0143] The template for the Mode A query signal is denoted as ATC_A(n).

[0144] The template for the C-mode query signal is denoted as ATC_C(n).

[0145] The template for the A / C mode query signal is denoted as ATC_A / C(n).

[0146] The template for the S-mode query signal is denoted as ATC_S(n).

[0147] Signal identification can be performed using the sliding difference. Then, by calculating the statistical characteristics of the impulse within the template signal, it can be determined whether it is an interrogation signal.

[0148] For example, for a query signal of mode A, the sliding difference algorithm is as follows:

[0149] Δ=∑x(Tn)-ATC_A(n)

[0150] Here, T stands for sliding window.

[0151] By analyzing the statistical properties of Δ, we can determine what kind of interrogation signal it is.

[0152] S10.1.1A Mode Interrogation Signal Recognition

[0153] When Δ is used to query the A-mode signal Δ A If they are similar, it can be determined that it is a Mode A query signal.

[0154] S10.1.2C Mode Interrogation Signal Recognition

[0155] When Δ is used to query the C mode signal Δ C If they are similar, it can be determined that it is a C-mode query signal.

[0156] S10.1.3A / C Mode Interrogation Signal Recognition

[0157] When Δ is used to query the A / C mode signal Δ A / C If they are similar, it can be determined that it is an A / C mode query signal.

[0158] S10.1.4S Mode Interrogation Signal Recognition

[0159] When Δ is used to query the S-mode signal Δ S If similarity is found, it can be determined to be an S-mode interrogation signal. It should be noted that, because S-mode interrogation signals contain encoded information, their extraction requires further demodulation via BPSK. This is not the method described in this invention.

[0160] S10.2 Gaussian pulse decoding

[0161] If the identified signal is a TACAN signal, it can be identified using a Gaussian pulse template based on the TACAN signal.

[0162] The TACAN system uses pulse code modulation (PCM) signals, employing Gaussian pulse signals and encoding them in pulse pairs. Different information is expressed using the interval between pulse pairs and the interval within each pulse pair. The mathematical expression for the Gaussian pulse signal is:

[0163]

[0164] In the formula, 'c' represents the steepness of the pulse, and 2ln2c is the width at half the peak amplitude. The form of the basic Tacanon pulse unit is as follows: Figure 9 As shown.

[0165] TCN signals are preprocessed by Gaussian modulation of the signals for ranging, direction finding, and identification audio signals.

[0166] S10.2.1 TACAN Arrival Signal Recognition

[0167] Signal identification is used to ensure that the signal from the current TACAN station matches the navigation direction of the local signal. If the matched signal is consistent with the received signal, it proves that the signal belongs to this navigation station; otherwise, it proves that it is not a TCN signal.

[0168] Signal identification uses pulse decoding to determine the signal. Pulse decoding mainly involves subtracting the received signal and accumulating the pulses to obtain relevant information.

[0169] At the same time, the pulse decoding signal simultaneously produces the ranging pulse and the reference pulse, thus providing certain data information for ranging and direction finding.

[0170] Pulse decoding pair

[0171]

[0172] Here, if p(n) is the matching pulse signal and N is the cumulative number of pulses, then based on the number of primary and secondary reference pulse pairs required for Tacon direction finding, we can obtain N = 12. By judging the Ω signal, we can draw the following conclusions:

[0173] Ω<Ω0: The signal is an amplitude envelope signal.

[0174] Ω>Ω1: Signal reference pulse and ranging echo signal

[0175] Where Ω1>Ω0, and their values ​​are based on empirical values ​​from the database.

[0176] S10.2.1.1 Peak Inspection

[0177] By decoding the template-matched signal through pulse decoding, an envelope signal composed of several pulse pairs can be obtained. The TACAN baseband signal consists of a Gaussian pulse and an envelope. The envelope is a modulated signal composed of sine waves at 15 Hz and 135 Hz. The presence of the TACAN signal can be confirmed by detecting the spectrum of the envelope of the received pulse stream. The TACAN baseband signal is formed by amplitude modulation of a Gaussian pulse by a composite field of 15 Hz and 135 Hz. The envelope is composed of the pulse vertices; therefore, the envelope curve can be constructed by determining the pulse peaks. The method for measuring the pulse peak is to compare the signal amplitude over a period of time (one pulse width) after the pulse arrival time, and take the maximum amplitude as the pulse peak point. These peak points constitute the envelope curve, such as... Figure 10 As shown.

[0178] S10.2.1.1.1, Mutual Demodulation

[0179] The signal pulse after peak detection is a composite signal composed of 135Hz and 35Hz. This composite signal can then be demodulated using coherent 135Hz and 35Hz signals respectively, yielding the envelope pulse signals of the two signals and thus the phase signal. The demodulation equation is:

[0180] f(n) = x(n)·cos(f0*n)

[0181] Where x(n) represents the signal envelope, and the cosine function represents the demodulation envelope, the resulting signals are a 135Hz signal envelope and a 35Hz signal envelope. Since the synthesized signal has complex signal frequency characteristics, it can also include a harmonic signal of 135+35Hz.

[0182] S10.2.1.1.1.1, 15Hz filter

[0183] Since the signal obtained from mutual demodulation has multi-spectral components, it is necessary to filter these components. Here, a low-pass filter can be used, with the filter bandwidth being the signal's spectral components. This allows us to obtain the cosine waveform of the signal, and thus determine the position of π / 2.

[0184] S10.2.1.1.1.2 135Hz Filter

[0185] Since the signal obtained from mutual demodulation has multi-spectral components, it is necessary to filter these components. Here, a low-pass filter can be used, with the filter bandwidth being the signal's spectral components. This allows us to obtain the cosine waveform of the signal, and thus determine the position of π / 2.

[0186] S10.2.1.2, Amplitude Limiting, Pulse Recognition

[0187] Here, the main constraint is the amplitude inconsistency, because the signal after pulse decoding and signal recognition has the characteristic of amplitude inconsistency. Therefore, to ensure the convenience of subsequent calculations, the amplitude can be normalized.

[0188] x(n)>S,x(n)=s0

[0189] This ensures consistency in amplitude.

[0190] Furthermore, by analyzing the characteristics of the pulse, according to the standard, it can be determined whether it is a main reference pulse, an auxiliary reference pulse, a ranging pulse, or a recognition tone pulse.

[0191] S10.2.1.2.1 Main reference pulse identification and decoding

[0192] For a pulse pair, the rule for x(n) to identify the master reference pulse is:

[0193] In X mode, the main reference pulse group consists of 24 pulses, which are paired into a pulse pair, for a total of 12 pulse pairs. The interval between the two pulses in each pulse pair is 12±0.1μs, and the interval between any two adjacent pulse pairs is 30±0.1μs.

[0194] In Y mode, the main reference pulse group consists of 13 pulses, with an interval of 30 ± 0.1 μs between adjacent pulses.

[0195] Using this pulse matching criterion, the main reference pulse and its mode can be determined, and the pulse time of the main reference pulse can be obtained as t. 主 .

[0196] S10.2.1.2.2, Auxiliary reference pulse identification and decoding

[0197] For a pulse pair, the rule for x(n) to identify the auxiliary reference pulse is:

[0198] In X mode, the auxiliary reference pulse group consists of 12 pulses, which are paired up to form a pulse pair, just like the main reference pulse group. There are a total of 6 pulse pairs. The interval between the two pulses in each pulse pair is 12±0.1μs, and the interval between any two adjacent pulse pairs is 24±0.1μs.

[0199] In Y mode, the auxiliary reference pulse group also consists of 13 pulses, but the interval between adjacent pulses is 15±0.1μs.

[0200] Using this pulse matching criterion, the auxiliary reference pulse and its mode can be determined. Furthermore, the pulse time of the main reference pulse is t. 辅 .

[0201] S10.2.1.2.3 Ranging Pulse Decoding and Recognition

[0202] For a pulse pair, the rule for x(n) to identify the ranging pulse is:

[0203] The distance response pulse consists of two pulses, with a pulse interval of 12±0.1μs in X mode and 30±0.1μs in Y mode. When the ground base station receives an interrogation signal from the airborne equipment, it will transmit a response pulse after a specified delay. This delay is 50±0.1μs in X mode and 56±0.1μs in Y mode. During the delay, the ground base station does not transmit other signals, but since the distance response pulse has a priority only higher than the padding pulse, other types of pulses will be transmitted first before the response delay is calculated.

[0204] This allows us to calculate the arrival time t of the distance response pulse. 测距 .

[0205] S10.2.1.2.4, Identification Pulse Decoding and Recognition

[0206] For a pulse pair, x(n) identifies the pulse according to the following rules:

[0207] The identification pulse consists of two pulses, with a pulse interval of 12±0.1μs in X mode and 30±0.1μs in Y mode. The identification pulse is synchronized with the azimuth reference pulse group, and there should be a uniform pulse with the same code as the identification pulse 100±10μs after it. The identification signal is obtained by sampling the identification pulse for several seconds.

[0208] S10.2.1.1.1.1.1, Coarse Azimuth Measurement

[0209] The coarse azimuth measurement is performed using the main reference pulse time t in S10.2.1.2.1. 主 The information of the 0-phase of the azimuth coarse pulse is used to obtain the corresponding time difference:

[0210] Δt 主 =min{t 主 -t n},(t 主 -t n )>0

[0211] In this way, the relative time Δt can be calculated. 主 The corresponding azimuth value can be calculated:

[0212]

[0213] S10.2.1.1.1.2.1 Precise Azimuth Measurement

[0214] Precise orientation measurement is achieved through the auxiliary quasi-pulse time t in S10.2.1.2.1. 主 The information of the 0-phase of the azimuth coarse pulse is used to obtain the corresponding time difference:

[0215] Δt 辅 =min{t 辅 -t n},(t 辅 -t n )>0

[0216] In this way, the relative time Δt can be calculated. 辅 The corresponding azimuth value can be calculated:

[0217]

[0218] The above description is merely a specific embodiment of the present invention, providing a detailed description of the invention. Parts not covered herein are conventional techniques. However, the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for integrated TCN and ATC signal recognition, characterized in that: Here are the steps: Step 1: Perform bandpass pre-filtering on the received RF signal, and extract the pre-filtered data into two channels; Step 2: Perform DFT processing on data A to obtain a complex domain spectrum, calculate the spectrum data, perform spectrum analysis based on the spectrum data to obtain the signal spectrum distribution, perform frequency domain sorting and filtering on the signals with TCN and ATC distributions to obtain the signals within the TCN and ATC bandwidths; perform delay processing on data B. Step 3: Based on the spectrum distribution information obtained from channel A, perform digital bandpass filtering on the data from channel B; Step 4: Perform coherent demodulation to obtain the I-channel signal and the Q-channel signal; Step 5: Delay the I-channel signal and the Q-channel signal respectively to obtain the I'-channel signal and the Q'-channel signal; Step 6: Superimpose the I' and Q' signals to obtain the Σ signal; Step 7: Perform pulse matched filtering on the Σ signal to identify the signal type. If it is an ATC signal, proceed to Step 8; if it is a TCN signal, proceed to Step 9. Step 8: Perform pattern recognition on the ATC signal; Step 9: Identify and decode the TCN signal.

2. The method according to claim 1, characterized in that: In step two, the DFT processing procedure is as follows: in, x(n) is the input digital signal. X(k) is used to calculate the peak value of each frequency point, which can effectively calculate the position of the current frequency point.

3. The method according to claim 2, characterized in that: In step three, the digital bandpass filter uses an FIR filter, the basic function of which is: Where x(n) is the input signal, h(n) is the matched filter template, and y(n) is the filtered spectrum data.

4. The method according to claim 3, characterized in that: In step five, the signal delay process involves selecting an optimal N-beat delay based on empirical parameters; N is no greater than the shortest length of an ATC or TCN pulse data.

5. The method according to claim 4, characterized in that: In step six, the formula for calculating the Σ signal is as follows: Σ=I'(n)+Q'(n).

6. The method according to claim 5, characterized in that: In step seven, the formula for calculating the pulse matched filter is as follows: Where N is the length of the digital domain of the signal, which is calculated based on the template of each signal; p(n)=∑P1(n-T1)+P2(n-T2)+P3(n-T3)+P5(n-T5) Where T1, T2, T3, and T5 represent the positions of the signal pulses, respectively.

7. The method according to claim 6, characterized in that: In step eight, the signal interrogation mode is identified based on the ATC signal template. The process is as follows: The input signal pulse sequence is s(n), the template for the A mode query signal is ATC_A(n), the template for the C mode query signal is ATC_C(n), the template for the A / C mode query signal is ATC_A / C(n), and the template for the S mode query signal is ATC_S(n). The input signal pulse sequence is subjected to sliding interpolation calculation using different templates to obtain the Δ signal, as shown in the following formula: Δ=∑s(Tn)-ATC_X(n) ATC_X(n) is ATC_A(n), ATC_C(n), ATC_A / C(n), or ATC_S(n); Determine whether the Δ signal is similar to the query signal of the corresponding template. If they are similar, then it is the query signal of the pattern corresponding to the template.

8. The method according to claim 7, characterized in that: The TCN signal identification and decoding process is as follows: A Gaussian pulse template based on TACAN signals is used for identification; the judgment is made through pulse decoding. Pulse decoding performs subtraction on the received signal and accumulates the pulses to obtain relevant information. The pulse decoding signal simultaneously yields the ranging pulse and the reference pulse; Pulse decoding pair p(n) is the matched pulse signal, N is the cumulative number of pulses, N = 12. By judging the Ω signal, the following conclusions can be drawn: Ω<Ω0: The signal is the amplitude envelope signal; Ω>Ω1: The signal reference pulse and ranging echo signal, where Ω1>Ω0, and the values ​​of the two are based on empirical values ​​from the database. The signal is matched to the template by pulse decoding to obtain an envelope signal composed of several pulse pairs. The presence of the TACAN signal is determined by detecting the spectrum of the envelope of the received pulse stream. The signal amplitudes are compared within one pulse width after the pulse arrival time, and the maximum amplitude is taken as the peak point of the pulse. These peak points form the envelope curve. The signal pulse after peak detection is a composite signal composed of 135Hz and 35Hz. The composite signal can be demodulated by coherent signals of 135Hz and 35Hz respectively to obtain the envelope pulse signals of the two signals, and then the phase signal. The signal obtained by mutual demodulation has multi-spectral components. Filtering the multi-spectral components yields the cosine signal waveform of the signal. After normalizing the amplitude, x(n) > S, x(n) = s0 For a pulse pair, the rule for x(n) to identify the master reference pulse is: In X mode, the main reference pulse group consists of 24 pulses, which are paired into a pulse pair, with a total of 12 pulse pairs. The interval between the two pulses in each pulse pair is 12±0.1μs, and the interval between any two adjacent pulse pairs is 30±0.1μs. In Y mode, the main reference pulse group consists of 13 pulses with an interval of 30±0.1μs between adjacent pulses; Using this pulse matching criterion, the main reference pulse and its mode are determined, and the pulse time of the main reference pulse is obtained as t. 主 ; For a pulse pair, the rule for x(n) to identify the auxiliary reference pulse is: In X mode, the auxiliary reference pulse group consists of 12 pulses, which are paired up in pairs, just like the main reference, for a total of 6 pulse pairs. The interval between the two pulses in each pulse pair is 12±0.1μs, and the interval between any two adjacent pulse pairs is 24±0.1μs. In Y mode, the auxiliary reference pulse group also consists of 13 pulses, but the interval between adjacent pulses is 15±0.1μs; This pulse matching criterion can be used to determine the auxiliary reference pulse and its mode; Furthermore, the pulse time for obtaining the main reference pulse is t. 辅 ; For a pulse pair, the rule for x(n) to identify the ranging pulse is: The distance response pulse consists of two pulses, with a pulse interval of 12±0.1μs in X mode and 30±0.1μs in Y mode. When the ground base station receives an interrogation signal from the airborne equipment, it will transmit a response pulse after a specified delay; this delay is 50±0.1μs in X mode and 56±0.1μs in Y mode. During the delay, the ground base station does not transmit other signals, but since the distance response pulse has a priority only higher than the padding pulse, it will transmit other types of pulses first before calculating the response delay; thus, the arrival time t of the distance response pulse is calculated. 测距 ; For a pulse pair, x(n) identifies the pulse according to the following rules: The identification pulse consists of two pulses, with a pulse interval of 12±0.1μs in X mode and 30±0.1μs in Y mode; The identification pulse is synchronized with the azimuth reference pulse group, and there should be a balanced pulse with the same code as the identification pulse 100±10μs after it; the identification signal is obtained by sampling the identification pulse for several seconds. The coarse azimuth measurement is performed using the main reference pulse time t in S10.2.1.2.

1. 主 The information of the 0-phase of the azimuth coarse pulse is used to obtain the corresponding time difference: Δt 主 =min{t 主 -t n },(t 主 -t n )>0 The relative time Δt was calculated. 主 The corresponding azimuth values ​​are obtained through conversion: By using the auxiliary quasi-pulse time t 主 The information of the 0-phase of the azimuth coarse pulse is used to obtain the corresponding time difference: Δt 辅 =min{t 辅 -t n },(t 辅 -t n )>0 relative time Δt 辅 The corresponding azimuth values ​​are obtained through conversion:

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