CN101825707B - Monopulse angular measurement method based on Keystone transformation and coherent integration - Google Patents

Abstract

The invention relates to a monopulse angular measurement method based on Keystone transformation and coherent integration, which comprises the steps of: (1) carrying out pulse compression on a video signal of a target echo; (2) carrying out the Keystone transformation on the signals subjected to the pulse compression so as to correct migration through range cell; (3) carrying out coherent integration on corrected pulse string; (4) carrying out target detection based on CFAR (Constant Fault Alarm Rate); and (5) extracting and computing target angular information. The invention can effectively improve the measurement precision on a target position and motion information and has wider applicability.

Description

Monopulse Angle Measurement Method Based on Keystone Transformation and Coherent Accumulation

technical field

The invention relates to a monopulse angle measurement method based on Keystone transformation and coherent accumulation, in particular to a method for realizing target angle measurement on a monopulse radar system based on an LFM-PD system, and belongs to the technical field of signal processing.

Background technique

With the development of electronic technology and the complexity of the combat environment, in order to meet the combat requirements of low-altitude and ultra-low-altitude penetration targets in modern warfare, it is necessary to further improve the radar's anti-clutter ability and various detection performances. The monopulse radar system based on the linear frequency modulation-pulse Doppler (LFM-PD) system combines all the advantages of LFM, PD radar and monopulse technology, and can effectively meet the above requirements.

PD radar was developed in the 1960s to solve the interference of strong ground clutter of airborne downward-looking radar, and it has become a widely used radar system in the national air defense intelligence network. The PD radar transmits and receives the pulse train signal, uses the Doppler filter bank to obtain the range-Doppler image of the echo, and extracts the relative distance and speed information between the target and the radar by constant false alarm detection. It can distinguish targets at different distances and radial velocities, and can effectively separate and detect moving targets and background clutter. The Linear Frequency Modulation (LFM) signal is easier to generate and process, and is currently the most mature pulse compression signal in engineering applications. LFM-PD radar combines the characteristics of pulse compression of LFM waveform and the advantages of PD system, which can obtain higher distance and velocity resolution at the same time, and improve the radar range.

Monopulse technology began in the late 1940s. It was first proposed to solve the high-precision tracking of radar, also known as simultaneous lobe angle measurement. This technology only needs to compare the same echo pulse received by multiple beams to obtain all the information of the target position. Compared with the early conical scanning system, the time to obtain the angle error information is short, so the angle measurement is fast and the data rate is high. ; It is not sensitive to fluctuations in the echo amplitude, and has high angle measurement accuracy and anti-interference ability.

Radar systems using traditional monopulse angle measurement algorithms can be classified according to their composition. A general monopulse system can be summarized into three parts in structure: angle sensor, angle information converter and angle discriminator. There are three basic types of angle sensors: amplitude ratio, phase ratio, and amplitude-phase synthesis; angle discriminators have three methods: amplitude method, phase method, and difference method. Therefore, monopulse systems can be divided into nine categories. However, there are only four classical methods for practical application: amplitude comparison (direct amplitude comparison), amplitude sum difference (sum and difference comparison), phase comparison (direct phase comparison), and phase sum difference. Among them, amplitude and difference monopulse angle measurement has been most widely used because of its relatively easy hardware implementation.

Contents of the invention

The object of the present invention is to provide a single-pulse angle measurement method based on Keystone transformation and coherent accumulation, in the case of high-speed movement of the target, it can effectively compensate for the movement across distance units, and avoid the impact of motion on the range-Doppler image. Therefore, the measurement accuracy of the radar system to the target position and motion information is improved.

The angle measurement method proposed by the present invention is realized on the monopulse radar system based on the LFM-PD system. The radar system transmits coherent LFM pulse trains, and receives the target echo signal, and obtains the distance of the echo through PD processing - Doppler images, from which target position and motion information is extracted.

The present invention proposes a monopulse angle measurement method based on Keystone transformation and coherent accumulation, which specifically includes the following steps:

(1) Echo signal pulse compression: for the LFM signal adopted in the present invention, realize this process by matched filtering: first do FFT to the system reference signal, get its spectrum conjugate to obtain the frequency domain response of the matched filter; then The target video echo signal is also transformed into the frequency domain by FFT; the echo spectrum is multiplied by the frequency domain response of the matched filter to obtain the frequency domain waveform of the matched filtered signal; finally, the filtered signal spectrum is subjected to IFFT to obtain The time-domain waveform of the pulse compression result;

(2) Keystone transform to correct the movement across the distance unit: first, perform FFT transformation on the echo pulse train after the pulse pressure to the frequency domain; Relevant, used to eliminate changes in spectral components and distributions caused by target motion; since the variables on the slow time axis are in discrete form, the sinc function interpolation technique is used to realize the above telescopic transformation; finally, each echo after interpolation Do IFFT of the pulse to obtain the echo pulse train corrected by the movement of the cross-distance unit;

(3) Coherent accumulation of corrected pulse trains: Due to the filtering characteristics of DFT, that is, DFT processing can be equivalent to a set of narrow-band Doppler filters. DFT implementation;

(4) Target detection based on CFAR: Since CFAR processing is performed in the Doppler domain obtained after coherent accumulation, it is first necessary to take the modulus of the Doppler spectrum of the signal corresponding to each range gate; in order to judge the unit to be detected Whether there is a target in , firstly, select the component units in the processing window by using the sliding window processing method; then, average all the reference units, and multiply by the parameter K to obtain the detection threshold; finally, the unit to be detected The data sampling is compared with the detection threshold: if it is greater than the threshold, it is considered that the target is found, otherwise it is considered that the target does not exist;

(5) Target angle information extraction and calculation: the present invention adopts the amplitude and difference type monopulse angle measurement algorithm to extract and calculate the angle information of the target: first, the system receiving antenna forms two 3dB intersecting antenna beams, and simultaneously detects the target echo signal Receive; the echo signals received by the two beams form the sum beam and the difference beam through the monopulse comparator; then, the output signals of the sum and difference receiving channels are respectively subjected to down-conversion and signal amplitude amplification and normalization; the two-way normalization The first signal undergoes signal processing processes such as pulse compression and correction accumulation in the steps mentioned above; finally, it is sent to the phase detector to obtain an error voltage, and the angle from which the target deviates from the antenna beam is calculated from the error voltage.

The present invention proposes a monopulse angle measurement method based on Keystone transformation and coherent accumulation, and its advantages and effects mainly lie in:

(1) The present invention applies Keystone transformation technology, under the situation of high-speed motion of the target, can effectively compensate the walking of the cross-distance unit, avoid the Doppler spectrum widening or even severe deformation caused by the target motion, thereby improving the radar system's detection of the target position and motion. The accuracy of measurement of information.

(2) The present invention can effectively improve the signal-to-noise ratio of the echo signal by using the pulse compression and coherent accumulation processing of the conventional PD radar while correcting the movement of the target cross-distance unit, thereby further improving the detection capability of the radar.

(3) The angle measuring method proposed by the present invention is based on the monopulse radar system of conventional LFM-PD system, only needs to carry out the software development of signal processing part, does not need to refit or upgrade hardware configuration and structure, thereby reduces this method to radar system Hardware requirements, with wider applicability.

(4) The present invention has the characteristics of low system software development cost, short period, easy maintenance and function upgrade.

Description of drawings

Figure 1 is a processing flow chart of the monopulse angle measurement method based on Keystone transformation and coherent accumulation.

Figure 2 is a flowchart of the pulse compression process.

Fig. 3 is a flowchart of Keystone transform processing.

FIG. 4 is a flowchart of coherent accumulation processing.

FIG. 5 is a flowchart of object detection processing.

Fig. 6 is a structure diagram of CFAR processing window.

Figure 7 is a structural diagram of the amplitude and difference monopulse system.

Fig. 8 is a pattern diagram of two beam receiving antennas.

Fig. 9 is a flow chart of angle measurement processing.

Detailed ways

Below in conjunction with the accompanying drawings, the specific technical solutions of the invention will be further described.

The monopulse angle measuring method proposed by the invention is realized on the monopulse radar system based on the LFM-PD system. The LFM-PD radar system transmits coherent LFM pulse trains and receives the target echo signal, and obtains the range-Doppler image (R-D image) of the echo through PD processing, from which the target position and motion information are extracted.

As shown in Figure 1, a kind of monopulse angle measuring method based on Keystone transformation and coherent accumulation processing of the present invention, it specifically comprises the following steps:

(1) Echo signal pulse compression: The radar system performs pulse compression processing on the video signal of the target echo to obtain high resolution in distance and improve the echo signal-to-noise ratio. The early single-frequency signal has a contradiction between the radar range and the distance resolution. In this invention, the most widely used LFM signal is used in engineering. It realizes nonlinear phase modulation through frequency modulation, and can obtain large signals at the same time. Time width and bandwidth, thus solving this problem. For LFM signals, this pulse compression process can be realized by matched filtering. As shown in Figure 2, in actual digital systems, matched filtering is generally implemented in the frequency domain.

First, fast Fourier transform (FFT) is performed on the radar system reference signal synchronized with the transmitted signal to obtain its frequency domain samples. Conjugate the frequency-domain sampling value to obtain the frequency-domain response of the matched filter. Then, for the target echo signal that has been down-converted to video, FFT is also performed to the frequency domain. The frequency domain signal of the target echo is multiplied by the frequency domain response of the matched filter to obtain the frequency domain waveform of the matched filtered signal. Finally, the frequency domain waveform of the filtered signal is subjected to inverse fast Fourier transform (IFFT), and the pulse compression result of the echo signal can be obtained.

After pulse compression processing, the LFM signal can compress the original wide pulse signal with a rectangular envelope into a narrow pulse signal with an Sa function envelope, and the position of the compressed pulse peak corresponds to the echo delay time of the target, which can be obtained by The peak position extracts the distance information of the target relative to the radar. For an LFM signal with a bandwidth of B and a pulse width of τ, the distance resolution can reach c/2B, where c is the speed of light; the amplitude gain obtained after processing is (Bτ is the time-width-bandwidth product of the signal), thus improving the signal-to-noise ratio of the echo.

(2) Keystone transformation corrects distance unit walking: According to the design principle of traditional PD radar, during the coherent accumulation of pulse trains, it is required that the distance walking caused by target movement should not exceed half the distance resolution unit. This is relatively easy to meet when narrowband radar or target moves at low speed, but for wideband radar and target moving at high speed, due to the high range resolution, it will be very serious for the target to move across the range resolution unit in the pulse train, making the echo after PD processing The Doppler spectrum of the target is broadened or even seriously deformed, which will affect the measurement accuracy of the target information extraction part in the subsequent processing. In order to solve this problem, the present invention adopts Keystone transformation to correct target distance walking. It is originally an algorithm for correcting target range curvature in synthetic aperture radar (SAR) imaging, and it can also achieve good results when applied to inverse synthetic aperture radar (ISAR) to correct target range migration and faint target detection.

As shown in FIG. 3 , the multiple echo pulses after the pulse compression processing in step (1) are referred to as an echo pulse train. Firstly, each pulse in the echo pulse train is transformed into the frequency domain by FFT respectively. Then, slow-time stretching transformation is performed on the frequency spectrum of all pulses in the echo pulse train, and the stretching range of this transformation is related to frequency. The slow time mentioned here refers to the time variable between the respective starting moments of multiple echo pulses in the pulse train relative to the starting time of the whole pulse train, which is recorded as t _n =nT _r , n=0, 1,..., N-1. Among them, T _r is the pulse repetition period, and N is the number of accumulated pulses. Suppose the virtual slow time variable t _m =mT′ _r , m=0, 1, ..., N-1, then the stretching transformation of the slow time axis can be expressed as:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Where f _c is the signal carrier frequency. It can be seen that the essence of this scaling transformation is to rearrange the position of each echo pulse spectrum on the slow time axis. The principle of rearrangement is to eliminate the changes of spectrum components and distribution caused by target motion as much as possible.

Since the values of the variables on the slow time axis are in discrete form, it is necessary to use the data interpolation method to realize the above scaling transformation. Typical data interpolation techniques for signals include linear interpolation techniques, first-order all-pass interpolation techniques, Lagrangian interpolation techniques, and sinc function interpolation techniques. Comprehensively consider calculation amount and interpolation precision, adopt sinc function interpolation technology in the present invention, provide calculation formula here:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; pc</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; pc</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; [</span>\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

In the formula, S _pc (f, n) and S _pc (f, m) are the spectrum of the nth or m echo pulse after the pulse pressure processing before and after the Keystone transform, respectively. Finally, IFFT is performed on each echo pulse after interpolation to obtain the echo pulse train corrected by the movement of the cross-distance unit.

(3) The coherent accumulation of the corrected pulse train: the coherent accumulation realizes the PD processing of the radar system, which is based on the observation of multiple echo pulses. During the pulse accumulation time, the Doppler frequency shift f _d caused by target motion remains unchanged. For the sampling points of different echo pulses on the same range unit, the Doppler frequency shift only causes phase changes, that is, these The sequence of sampling points constitutes a single-frequency signal with a carrier frequency of f _d , so coherent accumulation is equivalent to performing spectrum analysis on the single-frequency signal. In digital signal processing technology, due to the filtering characteristics of the discrete Fourier transform (DFT), that is, DFT processing can be equivalent to a set of narrow-band Doppler filters, the above process can be performed by performing coherent pulse trains along the slow time domain. DFT implementation, as shown in Figure 4.

Through coherent accumulation, the slow time-domain sampling of N echo pulses located in the same distance unit is transformed into a narrow pulse signal in the Doppler domain, and the pulse peak is located at the Doppler frequency shift f _d , which can be obtained by coherent accumulation The amplitude envelope of the Doppler domain signal obtains the velocity related information of the target. The Doppler resolution that can be achieved after coherent accumulation is 1/NT _r ; the maximum Doppler frequency shift that can be measured is (N-1)/NT _r , if it is greater than this value, velocity defuzzification processing is required .

The signal-to-noise ratio can also be effectively improved by coherent accumulation. Since the accumulation of multiple pulses enhances the received echo energy, the processing gain can reach the number N of accumulated pulses at the peak value of the pulse corresponding to the position of the target, which can further improve the detection capability of the radar.

(4) Target detection based on CFAR: In order to detect moving targets of interest in complex clutter environments, PD radars usually use constant false alarm rate (CFAR) processing technology. CFAR is a digital signal processing algorithm that provides a detection threshold. When the noise background clutter and interference change, it can make the target detection have a constant false alarm probability under the premise of ensuring a certain detection probability.

As shown in Fig. 5, the CFAR processing is to detect in the Doppler domain obtained after the coherent accumulation processing in step (3), so the signal Doppler spectrum corresponding to each range gate must first be moduloed. In the actual digital system, since it is unknown which Doppler resolution unit the moving target is in, CFAR adopts a sliding window processing method. Figure 6 shows the composition structure of a CFAR processing window: the unit to be checked is located in the center of the processing window; the units adjacent to the two sides of the unit to be checked are called protection units, and the corresponding data samples are not used for noise parameter estimation to reduce The self-interference formed by the target crossing adjacent Doppler cells; the protection cells are flanked by reference cells, and the corresponding data samples are used to estimate the noise parameters. The unit to be checked, the protection unit and the reference unit together form the CFAR processing window. In a CFAR detection, in order to judge whether there is a target in the unit to be detected, the constituent units in the processing window must be selected first, then all reference units are averaged, and at the same time multiplied by the parameter K to obtain the detection threshold. The value of the parameter K here is determined by the distribution law of the environmental noise and the detection probability and false alarm probability to be achieved by the system. Finally, compare the data sampling of the unit to be detected with the detection threshold: if it is greater than the detection threshold, it is considered that the target is found, and subsequent data processing can be performed; otherwise, the target is considered not to exist.

(5) Extraction and calculation of target angle information: the present invention uses the amplitude and difference monopulse angle measurement algorithm to extract and calculate the angle information of the target. The monopulse system structure based on the algorithm is shown in Figure 7. First, the primary feed of the radar system receiving antenna forms two 3dB intersecting antenna beams as shown in Figure 8, and simultaneously receives target echo signals. As shown in Figure 7 and Figure 9, the echo signals received by the two beams are formed by a single-pulse comparator into a sum beam and a difference beam. Then, the output signals of the sum and difference receiving channels are respectively down-converted, and the signal amplitude is amplified and normalized by the amplifier and the automatic gain control (AGC) circuit. The two normalized signals go through signal processing processes such as pulse compression and correction accumulation in steps (1) to (4) above, and are finally sent to the phase detector. The output voltage Δu of the phase detector is called the error voltage, which contains the target angle information, and its expression is given here:

或π，由目标位置偏离和波束最大值的方向决定。用高斯函数对两天线波束进行拟合，从而可由和、差信号比值计算得到目标偏离天线波束指向的角度Δθ：In the formula, F _∑ (θ) and F _Δ (θ) are the output voltages of sum and difference channels respectively,

or π, determined by the target position deviation and the direction of the beam maximum. The Gaussian function is used to fit the two antenna beams, so the angle Δθ at which the target deviates from the antenna beam can be calculated from the ratio of the sum and difference signals:

$\mathrm{<span\; class="notranslate">\; \&Delta;\&theta;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&alpha;\&Delta;</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 0.5</span>}}}\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;u</span>}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;u</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

Among them, the parameter The parameter Δθ _0.5 is the half-power point width of the antenna beam. It can be seen that the angle measurement accuracy of this single-pulse angle measurement method is affected by the error voltage measurement accuracy and the antenna beam width.

Claims (1)

1. A monopulse angle measurement method based on Keystone transformation and coherent accumulation, characterized in that: the method specifically comprises the following steps: (1) Echo signal pulse compression: for the LFM signal adopted in the present invention, realize this process by matched filtering: first do FFT to the radar system reference signal synchronous with the transmitted signal, obtain the frequency domain sampling value, and this frequency domain The domain sampling value is conjugated to obtain the frequency domain response of the matched filter; then the target video echo signal is also transformed into the frequency domain by FFT; the video signal of the target echo is multiplied by the frequency domain response of the matched filter to obtain The frequency-domain waveform of the signal after matching filtering; finally, perform IFFT on the frequency-domain waveform of the filtered signal to obtain the pulse compression result of the echo signal; (2) Keystone transform to correct the movement across the distance unit: the multiple echo pulses after the pulse compression processing in step (1) are called echo pulse trains; firstly, each pulse in the echo pulse train is transformed into frequency domain; then perform slow-time stretching transformation on the spectrum of all pulses in the echo pulse train, and its stretching range is related to frequency, so as to eliminate the change of spectrum components and distribution caused by target motion; because the variables on the slow time axis take The value is in a discrete form, and then the sinc function interpolation technology is used to realize the above telescopic transformation; finally, IFFT is performed on each echo pulse after interpolation to obtain the echo pulse train corrected by walking across the distance unit; (3) Coherent accumulation of the corrected pulse train: due to the filtering characteristics of DFT, that is, DFT processing can be equivalent to a set of narrow-band Doppler filters, and the coherent accumulation process of the corrected pulse train can be achieved through the coherent The pulse train is implemented by DFT along the slow time domain; (4) Target detection based on CFAR: Since the CFAR processing is to detect in the Doppler domain obtained after the coherent accumulation of step (3), it is first necessary to take the modulus of the signal Doppler spectrum corresponding to each range gate; In order to judge whether there is a target in the unit to be detected, firstly, the sliding window processing method is used to select the constituent units in the processing window; then, all reference units are averaged, and the parameter K is multiplied at the same time to obtain the detection threshold; finally, Compare the data sampling of the unit to be detected with the detection threshold: if it is greater than the threshold, it is considered that the target is found, otherwise it is considered that the target does not exist; Among them, the value of parameter K is determined by the distribution law of environmental noise and the detection probability and false alarm probability to be achieved by the system; (5) Target angle information extraction and calculation: the present invention adopts the amplitude and difference type monopulse angle measurement algorithm to extract and calculate the angle information of the target: first, the radar system receiving antenna forms two 3dB intersecting antenna beams, and the target echo is simultaneously The signal is received; the echo signals received by the two beams form the sum beam and the difference beam through the monopulse comparator; then, the output signals of the sum and difference receiving channels are respectively subjected to down-conversion and signal amplitude amplification and normalization; two channels The normalized signal goes through the pulse compression and correction accumulation signal processing in the steps mentioned above; finally, it is sent to the phase detector to obtain the error voltage, and the angle of the target deviating from the antenna beam is calculated by the error voltage.

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