CN113358931B - Time difference calculation method based on cross-power spectrum - Google Patents
Time difference calculation method based on cross-power spectrum Download PDFInfo
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Abstract
The invention discloses a time difference calculation method based on cross power spectrum, which comprises the following steps: and the plurality of receiving stations receive the radio frequency signals, and the radio frequency signals are forwarded and summarized to the receiving processing extension set of the main station in real time through the microwave relay. After being subjected to down-conversion synchronously in the receiving and processing extension of the master station, a plurality of radio frequency signals enter the acquisition and processing module for sampling. The acquisition processing module respectively sends the multi-station sampling data to a filter and a signal processing FPGA; and opening a delay line in a signal processing FPGA, and caching multi-station sampling data in a time window in real time in a pipeline manner. After the filter respectively detects the multi-station sampling data in parallel, the filter guides the flow sampling data on the delay line to calculate the cross power spectrum of the multi-station sampling data in real time by windowing, and the cross power spectrum of the multi-station sampling data is synchronously multiplied by respective correction weighting factors to obtain the time difference of the same pulse signal in the multi-path data. The invention improves the time difference calculation and positioning precision of the multi-station single-pulse signal.
Description
Technical Field
The invention relates to the field of signal time difference calculation, in particular to a time difference calculation method based on a cross-power spectrum.
Background
With the development of modern weapon systems, electronic warfare has become increasingly more prevalent in modern war. The war under modern high-tech conditions is an integrated three-dimensional war in a plurality of fields of sea, land, air, sky, electromagnetism and the like. The rapid and accurate determination of the position of a spatial target is a fundamental task of military surveillance detection systems. The passive warning detection system does not radiate electromagnetic waves, utilizes electromagnetic wave signals radiated by the target to detect and position the target, has the characteristics of good concealment and difficult discovery by enemies, and has important significance for preventing the target from being attacked by anti-radiation missile weapons, ensuring the safety of detection system equipment and operators and improving the survivability of the warning detection system in future wars.
The passive detection system extracts information such as distance, direction, flight path and the like of a target by receiving and processing a radiation signal generated by electronic information equipment carried by the target such as an airplane, a ship and the like. With the trend of increasing emphasis on concealed attack and hard killing functions of military electronic systems, passive object detection means take an increasingly important position in military guard detection systems. Passive detection systems play an important role in modern high-tech warfare. With the development of a novel passive detection positioning system technology, how to realize more accurate tracking and positioning of a target and enable the passive detection positioning system to exert greater combat effectiveness is a subject worth deep discussion.
In a two-dimensional plane, the time difference of the radiation source signal reaching the two measurement base stations draws a pair of hyperbolas taking the two stations as focuses. If three measuring base stations are used for forming two curves, two intersection points of two pairs of hyperbolas can be obtained, and then other auxiliary information is used for removing positioning ambiguity, so that the position of the radiation source can be determined. The time difference of the radiation source signals arriving at the two measuring base stations defines a pair of hyperboloids with two stations as focuses, and if any radiation source in a three-dimensional space is to be determined, at least four stations are required to form three single-side hyperboloids to generate intersection points so as to determine the position of the radiation source.
In the time difference positioning system, the position of the airplane is determined by mainly adopting a passive positioning method, particularly a passive time difference positioning method to measure the arrival time difference between signals, so as to detect, track and position a target. The passive time difference positioning method has the advantages of long acting distance, good concealment performance, certain positioning accuracy and the like, is not directly related to the frequency of a target radiation signal, and is slightly influenced by the specific form of target radiation. However, the time difference positioning method has larger initial estimation error and higher real-time requirement, and has poor passive positioning performance on low targets and low accuracy of multi-station time difference positioning.
Disclosure of Invention
The invention aims to solve the technical problems that when the time difference of arrival between measurement signals is calculated in the prior art, the initial estimation error is large, the real-time requirement is high, the passive positioning performance of a low target is poor, and the accuracy of multi-station time difference positioning is low.
A time difference calculation method based on cross-power spectrums comprises the following steps:
1) a plurality of receiving stations receive radio frequency signals, and the radio frequency signals are forwarded and summarized to a receiving processing extension set of a main station in real time through a microwave relay;
2) after synchronously performing down-conversion on a plurality of radio frequency signals in the master station receiving and processing extension, entering an acquisition processing module, and performing sampling conversion in a time window to obtain multi-station sampling data;
3) the acquisition processing module respectively sends the multi-station sampling data to a filter and a signal position FPGA; the filter respectively detects the multi-station sampling data in parallel; opening a delay line in a signal processing FPGA, and caching multi-station sampled data in a time window in real time in a flowing way;
4) after the multi-station sampled data are respectively detected in parallel, windowing is carried out to guide the pipeline sampled data on the delay line, and the cross power spectrum of the multi-station sampled data is calculated in real time;
5) and multiplying the cross-power spectrum synchronization of the multi-station sampling data by respective correction weighting factors to obtain the time difference of the same pulse signal in the multi-station sampling data.
The filter respectively detects the sampling data in parallel by the following specific steps:
3 a: the filter divides the sampling data into a plurality of sub-channels according to frequency bands by a digital channelization algorithm; calculating each channel of channelized output data, energy accumulation point number N, energy threshold value K and minimum continuous threshold value passing point number P through a channelized algorithm;
3b, sliding each channel of channelized output data point by point, calculating the energy E of adjacent N points, and comparing the E with the K;
3c, if E > K appears for continuous P times, detecting the existence of the signal, recording the position label of the E > K for the first time, and estimating the pulse arrival time TOA of the radio frequency signal;
and 3d, recording the occurrence position of the last E > K, and estimating the pulse width of the radio frequency signal.
Further, the channelization algorithm is a digital channelization algorithm based on a uniform IFFT filter bank.
Further, the step 4) specifically comprises:
4 a: performing serial real-time pipelined FFT conversion on the multi-station sampled data cached on the delay line to obtain an FFT result of the multi-station sampled data;
4 b: and carrying out conjugate multiplication operation on FFT results of the multi-station sampling data point by point in real time to obtain a cross-power spectrum of the multi-station sampling data.
Further, in the step 4a, the number of points of FFT is 1024 points.
Further, the step 5) specifically comprises:
modifying the weighting function W (omega) in the CSP algorithm to
λ is more than or equal to 0.5 and less than or equal to 1, G xy (ω) is the cross-power spectral function, ω is the signal frequency;
in a signal processing FPGA, a CORDIC algorithm is adopted, when lambda is calculated to be 0.75 in real time, correction weighting factors of a CSP algorithm are calculated, cross-power spectrums of multi-station sampling data are synchronously multiplied by the respective correction weighting factors, and the final cross-power spectrum value of each sampling data is obtained;
calculating the real-time phase of the final cross-power spectrum of each sampling data by using a CORDIC algorithm again; and in an effective time window, performing peak searching processing on the real-time phase of the final cross-power spectrum of each sampling data to obtain a peak value, wherein the peak value is a time difference value between two stations of the pulse signal.
Furthermore, the acquisition processing module is a 1GHz sampling AD, the processed signal is 750MHz intermediate frequency, and the instantaneous processing signal bandwidth is 400 MHz.
The invention has the beneficial effects that: the time difference between signals is calculated by a cross-power spectrum phase method, and a generalized cross-power spectrum phase time difference estimation method is formed by correcting the correction of a weighting function in a CSP algorithm, so that the performance of the CSP method under the environment noise of more than moderate degree is improved. The modified CSP algorithm has the capability of resisting noise interference, and obtains higher time difference estimation precision even at low signal-to-noise ratio. The algorithm can realize synchronous parallel operation, realizes the time difference calculation of multiple stations (more than or equal to 3 stations), accurately obtains the time difference value between multiple channels within limited time, and has high precision of multi-station time difference positioning.
Drawings
Fig. 1 is a flowchart of a time difference calculation method based on cross-power spectrum according to the present invention.
Fig. 2 is a structural diagram of a digital channelization algorithm of a filter in the time difference calculation method based on cross power spectrum according to the present invention.
Fig. 3 is a graph showing the relationship between the root mean square error and the signal-to-noise ratio estimated by the CSP method time difference calculation method according to the present invention.
Fig. 4 is a graph showing the relationship between the root mean square error and the signal-to-noise ratio estimated by the CSP method time difference calculation method when λ is 0.75.
Detailed Description
The time difference calculation method based on the cross-power spectrum, as shown in fig. 1, includes the following steps:
1) and the plurality of receiving stations receive the radio frequency signals, and the radio frequency signals are forwarded and summarized to the receiving processing extension set of the main station in real time through the microwave relay. The microwave amplifies the radio frequency signal, so that the amplified radio frequency signal can carry out omnibearing coverage on a coverage area.
2) After synchronously performing down-conversion in the master station receiving and processing extension, a plurality of radio frequency signals enter an acquisition processing module, and are subjected to sampling conversion in a time window to obtain multi-station sampling data. The acquisition processing module preferably selects 1GHz sampling AD, the processed signal is 750MHz intermediate frequency, and the instantaneous processing signal bandwidth is 400 MHz; the sampling AD sampling rate of 1GHz is as high as 5GSa/s, and the transmission rate of system signals is increased.
3) The acquisition processing module respectively sends the multi-station sampling data to a filter and a signal position FPGA; the filter respectively detects the multi-station sampling data in parallel; a delay line is opened in a signal processing FPGA, multi-station sampling data in a real-time flow buffering time window is calculated according to the maximum distance of 30km between two base stations, the required delay line time is about 100us, and a buffer RAM enough for 100us flow is opened in the signal processing FPGA in an algorithm.
The filter respectively detects the multi-station sampling data in parallel according to the following steps:
3 a: the filter divides the sampling data into a plurality of sub-channels according to frequency bands by a digital channelization algorithm; and calculating each channel of channelized output data, the number N of energy accumulation points, the number K of energy threshold values and the number P of minimum continuous threshold value passing points through a channelizing algorithm. The signal detection adopts a digital channelization technology to complete channel division, which is a very important ring in the design of a channelization receiver, and the performance of the signal detection is good and bad, which directly influences the design difficulty of a subsequent processor. The invention preferably selects the FIR digital filter, the FIR digital filter system is stable, the linear phase is easy to realize, the multichannel filter is allowed to be designed, the pass band width pi/K and the stop band width 2 pi/K of the filter are set, and in order to cover the whole frequency domain, the filter is overlapped at the position of 3dB by adopting a 50% overlapping mode. The channelizing algorithm is a digital channelizing algorithm based on a uniform IFFT filter bank, and the structure of the channelizing algorithm is shown in fig. 2, where the output of the channelized receiver is:
y k (n) may be represented by an inverse discrete fourier transform. It can be seen that the fourier transform can be used for the implementation of a digital channelized filter bank. The calculation can be performed using IFFT, which can reduce the amount of calculation.
And 3b, sliding each channel of channelized output data point by point, calculating the energy E of the adjacent N points, and comparing the E with the K.
And 3c, if E > K appears for P times continuously, detecting the existence of the signal, and recording the position label of the E > K for the first time for estimating the pulse arrival time TOA of the radio frequency signal.
And 3d, recording the last E > K occurrence position for estimating the pulse width of the radio frequency signal.
4) After the multi-station sampling data are respectively detected in parallel, the window is opened to guide the flow sampling data on the delay line to calculate the cross-power spectrum of the multi-station sampling data in real time. The method comprises the following specific steps:
4 a: and performing serial real-time pipeline FFT (fast Fourier transform) on the multi-station sampling data cached on the delay line to obtain an FFT result of the multi-station sampling data. The invention preferably selects 1024-point serial real-time pipelined FFT to meet the requirement of throughput rate in the data processing process.
4 b: and carrying out conjugate multiplication operation on FFT results of the multi-station sampling data point by point in real time to obtain a cross-power spectrum of the multi-station sampling data.
5) And multiplying the cross-power spectrum synchronization of the multi-station sampling data by respective correction weighting factors to obtain the time difference of the same pulse signal in the multi-path data.
According to wiener-cinchona theorem, the correlation function of a signal and its power spectral density function are fourier transformed. Thus, the similarity between signals can be compared both in the time domain by the correlation function and in the frequency domain by the power spectral density function.
Cross-power spectral function G of received signals x (t) and y (t) xy (omega) is
G xy (ω)＝X(ω)Y * (ω)
＝S(ω)S * (ω)e jωD +S(ω)N y * (ω)
+S * (ω)e jωD N x (ω)+N x (ω)N y * (ω)
Wherein X (ω) and Y (ω) represent the Fourier transforms of X (t) and Y (t), respectively.
X(ω)＝FT{x(t)}＝S(ω)+N x (ω)，Y(ω)＝FT{y(t)}＝S(ω)e -jωD +N y (ω)，Y(-ω)＝Y * (ω)，
The operator FT stands for Fourier transform, S (omega), N x (omega) and N y (ω) source signal s (t), noise n x (t) and n y (t) Fourier transform.
Under the assumptions s (t), n x (t) and n y (t) on the premise of mutual independence, the last three terms of the above formula are zero, and the cross-power spectral function G xy (ω) can be simplified to:
G xy (ω)＝X(ω)Y * (ω)＝R s (ω)e -jωD
wherein R is s (ω)＝S(ω)S * (ω) is the real number of the power spectrum of the source signal S (t), and S (- ω) is S * (ω). As can be seen from the above formula, the time difference information D is contained in the cross spectrum G xy In the phase of (ω), Φ (ω) is ω D, j is an imaginary unit, and ω is a signal frequency. e.g. of the type -jωD ＝G xy (ω)/|G xy (ω) |, from which it is not difficult to see that the cross-power spectrum phase method (CSP method) is equivalent to PHAF in the generalized cross-correlation method (GCC method), and the phase transformation weighting function is a phase transformation weighting function;
in an actual environment, there tends to be large ambient noise, so that the last three terms of the above expression become large. With 1/| G xy (ω) | to approximate R s (ω) will have a large error so that the peak of the cross-correlation function is no longer significant, greatly affecting the performance of the CSP method.
Aiming at the problem that the CSP method has performance reduction under the environment noise of more than moderate degree, the CSP method is corrected to form generalized cross-power spectrum phase difference estimationAnd (4) counting. As the signal-to-noise ratio decreases, R s (omega) in G xy The proportion of (ω) decreases. Modifying the weighting function W (omega) in the CSP algorithm to
In the above formula, when λ ═ 1, the CSP method is used; if λ is 0.75, it is another CSP method in a broad form.
In the FPGA for signal processing, a CORDIC algorithm is adopted, when lambda is calculated to be 0.75 in real time, correction weighting factors of a CSP algorithm are used, cross-power spectrums of multi-station sampling data are synchronously multiplied by the correction weighting factors, and the final cross-power spectrum value of each sampling data is obtained. At low snr, λ is 0.75, the correction algorithm has the ability to resist noise interference, and still obtain high moveout estimation accuracy even at low snr. Under the same condition, the calculation accuracy of the multi-station single-pulse time difference is optimized from 17.3ns (RMS) to 3.6ns (RMS), and the accuracy of the multi-station single-pulse time difference positioning is greatly improved.
Calculating the real-time phase of the final cross-power spectrum of each sampling data by using a CORDIC algorithm again; and in an effective time window, performing peak searching processing on the real-time phase of the final cross-power spectrum of each sampling data to obtain a peak value, wherein the peak value is a time difference value between two stations of the pulse signal. The algorithm can synchronously perform parallel operation, realizes the time difference calculation of a plurality of stations (more than or equal to 3 stations), and accurately obtains the time difference value between multiple channels within limited time.
The invention provides a time difference calculation system based on a cross power spectrum, which realizes a time difference calculation method based on the cross power spectrum, and comprises a plurality of receiving stations, a master station and microwave relays, wherein the receiving stations are in wireless communication connection with the master station through the microwave relays respectively; receive and handle extension output and acquisition and processing module input electric connection, wave filter input and FPGA input respectively with acquisition and processing module output electric connection. The signal processing FPGA is provided with a delay line.
The specific implementation mode is as follows:
the duration of the rising edge of the pulse is 300ns, the duration of the top of the pulse is 0.5us, the duration of the falling edge is 300ns, the carrier frequency is 750MHz, the signal-to-noise ratio is 5-31dB, and the sampling rate is 1.0 GHz. Calculating time difference errors of two paths of signals of different intra-pulse modulation types, and correcting a weighting function of the CSP algorithm; when λ is 1, the relation curve of the estimated root mean square error and the signal-to-noise ratio is shown in fig. 3 by the CSP method time difference calculation method; when λ is 0.75, the root mean square error is estimated as a function of the signal-to-noise ratio by the CSP method time difference calculation method as shown in fig. 4. At low signal-to-noise ratios, λ 0.75 outperforms λ 1. The modified CSP algorithm has the capability of resisting noise interference, and obtains higher time difference estimation precision even at low signal-to-noise ratio.
It should be understood that the above-described embodiments are merely preferred examples of the present invention and the technical principles applied thereto, and any changes, modifications, substitutions, combinations and simplifications made by those skilled in the art without departing from the spirit and principle of the present invention shall be covered by the protection scope of the present invention.
Claims (7)
1. A time difference calculation method based on cross-power spectrums comprises the following steps:
1) a plurality of receiving stations receive radio frequency signals, and the radio frequency signals are forwarded and summarized to a receiving processing extension set of a main station in real time through a microwave relay;
2) a plurality of radio frequency signals synchronously undergo down-conversion in a master station receiving and processing extension, then enter an acquisition processing module, and are subjected to sampling conversion in a time window to obtain multi-station sampling data;
3) the acquisition processing module respectively sends the multi-station sampling data to a filter and a signal position FPGA; the filter respectively detects the multi-station sampling data in parallel; opening a delay line in a signal processing FPGA, and caching multi-station sampling data in a time window in real time in a pipeline manner;
4) after the multi-station sampling data are respectively detected in parallel, windowing is conducted to guide the running water sampling data on the delay line, and cross-power spectrums of the multi-station sampling data are calculated in real time;
5) multiplying the cross-power spectrum synchronization of the multi-station sampling data by respective correction weighting factors to obtain the time difference of the same pulse signal in the multi-station sampling data;
the method is characterized in that: the filter respectively detects the sampling data in parallel, and the method comprises the following specific steps:
3 a: the filter divides the sampling data into a plurality of sub-channels according to frequency bands by a digital channelization algorithm; calculating each channel of channelized output data, energy accumulation point number N, energy threshold value K and minimum continuous threshold value passing point number P through a channelized algorithm;
3b, sliding each channel of channelized output data point by point, calculating the energy E of adjacent N points, and comparing the E with the K;
3c, if E > K appears for continuous P times, detecting the existence of the signal, and recording the position label of the E > K for the first time for estimating the pulse arrival time TOA of the radio frequency signal;
and 3d, recording the occurrence position of the last E > K, and estimating the pulse width of the radio frequency signal.
2. The cross-power spectrum-based time difference calculation method according to claim 1, wherein: the channelizing algorithm is a digital channelizing algorithm based on a uniform IFFT filter bank.
3. The cross-power spectrum-based time difference calculation method according to claim 1, wherein: the step 4) is specifically as follows:
4 a: performing serial real-time pipeline FFT (fast Fourier transform) on the multi-station sampling data cached on the delay line to obtain an FFT result of the multi-station sampling data;
4 b: and carrying out conjugate multiplication operation on FFT results of the multi-station sampling data point by point in real time to obtain a cross-power spectrum of the multi-station sampling data.
4. A cross-power spectrum based moveout calculation method as claimed in claim 3, wherein: in step 4a, the number of points of FFT is 1024 points.
5. The cross-power spectrum-based time difference calculation method according to claim 1, wherein: the step 5) is specifically as follows:
modifying the weighting function W (omega) in the CSP algorithm to
λ is more than or equal to 0.5 and less than or equal to 1, G xy (ω) is the cross-power spectral function, ω is the signal frequency;
in a signal processing FPGA, a CORDIC algorithm is adopted, when lambda is calculated to be 0.75 in real time, correction weighting factors of a CSP algorithm are calculated, cross-power spectrums of multi-station sampling data are synchronously multiplied by the respective correction weighting factors, and the final cross-power spectrum value of each sampling data is obtained;
calculating the real-time phase of the final cross-power spectrum of each sampling data by using a CORDIC algorithm again; and in an effective time window, performing peak searching processing on the real-time phase of the final cross-power spectrum of each sampling data to obtain a peak value, wherein the peak value is a time difference value between two stations of the pulse signal.
6. A cross-power spectrum based time difference calculation method as claimed in claim 1, wherein: the acquisition processing module is 1GHz sampling AD, the processed signal is 750MHz intermediate frequency, and the instantaneous processing signal bandwidth is 400 MHz.
7. The cross-power spectrum-based time difference calculation method according to claim 1, wherein: the filter is an FIR digital filter.
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Denomination of invention: A time difference calculation method based on cross power spectrum Effective date of registration: 20221206 Granted publication date: 20220823 Pledgee: Shenzhen Rural Commercial Bank Co.,Ltd. Gongming Sub branch Pledgor: Shenzhen Huachuang Electric Technology Co.,Ltd. Registration number: Y2022980025226 |