Target detection and distance-Doppler parameter estimation method based on matched filtering

Abstract

The invention discloses a target detection and distance-Doppler parameter estimation method based on matched filtering, and provides the target detection and distance-Doppler parameter estimation method based on matched filtering, and the method comprises the steps: generating a plurality of wave beams in a detection range through a sonar system, so as to obtain information of different angle dimensions; and filtering information of different angle dimensions by using a matched filter, and reasonably selecting a transmitted waveform and a corresponding ambiguity function to match distance-Doppler parameters. Different angle information is obtained by utilizing array processing gain and beam forming, and matched filtering is performed on a received signal, so that parameters such as the distance and the radial speed of a target can be obtained while the signal-to-noise ratio of target detection can be improved. By using the method, the range resolution and the signal-to-noise ratio can be effectively improved, in addition, combined waveform multi-pulse processing is combined to carry out target distance-Doppler joint estimation, the reliability of target detection is further improved, and meanwhile, the signal-to-noise ratio is further enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of sonar target detection, and in particular relates to a target detection and range-Doppler parameter estimation method based on matched filtering. Background Art

[0002] Sonar is a technology or equipment that uses the propagation and reflection characteristics of sound waves in water to navigate and measure distance through electroacoustic conversion and information processing. Sonar can not only be used in the military field, such as the detection, identification, tracking, positioning and navigation of submarines and surface ships, but also in torpedo guidance, mine fuses, offshore oil exploration, ship navigation, underwater operations, hydrographic surveys and seabed geological surveys. Dual-frequency sonar is a sonar with two working frequencies. These two frequencies correspond to different detection angles, thereby improving the accuracy and efficiency of detection, and can provide high-precision and high-resolution detection results, suitable for various underwater detection tasks.

[0003] In the sonar system, a series of sound waves are first emitted through the transducer, and these sound waves are usually emitted in the form of ultrasound. Sound waves propagate at a certain speed in the water. When the sound waves encounter underwater objects, they are emitted back to form echoes. These echoes are then captured by the receiving part of the sonar system, so the reception of echoes is the key to obtaining target detection. When the sonar is detecting a target, the sonar's range-Doppler parameters are information parameters used to describe the target's distance and speed. The Doppler parameter estimation of sonar is the process of estimating parameters such as the speed, angle, and distance of the detected target object. However, the current estimation method does not have a good signal-to-noise ratio and distance resolution when detecting target objects. Therefore, there is an urgent need for a target detection and range-Doppler parameter estimation method that can have a high signal-to-noise ratio and distance resolution. Summary of the invention

[0004] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] In view of the problems and shortcomings in the prior art, the present invention aims to provide a target detection and range-Doppler parameter estimation method based on matched filtering. By matching filtering the received signal, the present invention can obtain parameters such as the target's range and radial velocity while improving the target detection signal-to-noise ratio, thereby enhancing the performance of target detection and identification, and having a good signal-to-noise ratio and range resolution. This is used to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention discloses a target detection and range-Doppler parameter estimation method based on matched filtering, including the following steps:

[0008] Step 1, the sonar system generates multiple beams within the detection range to obtain information in different angular dimensions;

[0009] Step 2, filter the information in different angular dimensions using a matched filter;

[0010] Step 3, reasonably select the transmitted waveform signal according to the ambiguity function to match the range-Doppler parameters.

[0011] Further, in step 2, filtering the obtained information in different angular dimensions using a matched filter specifically further includes the following steps:

[0012] Step 2.1, rewrite the signals in different angular dimensions into the form of the sum of the transmitted signal and noise;

[0013] Step 2.2, filter the information in different angular dimensions in the rewritten form using a linear time-invariant filter;

[0014] Step 2.3, perform an inverse Fourier transform on the filtered information in different angular dimensions;

[0015] Step 2.4, obtain the maximum signal-to-noise ratio in the frequency domain using a suitable matched filter;

[0016] Step 2.5, calculate the gain of the signal-to-noise ratio before and after matched filtering in combination with the maximum signal-to-noise ratio.

[0017] Further, in step 3, reasonably select the transmitted waveform and the corresponding ambiguity function to match the range-Doppler parameters, and the selection rule is:

[0018] The ambiguity function corresponding to the single-frequency pulse signal is suitable for estimating the velocity of the target and not suitable for measuring the target range;

[0019] The ambiguity function corresponding to the linear frequency modulation signal is suitable for measuring the target range and not suitable for target detection;

[0020] The ambiguity function corresponding to the two-tone frequency modulation signal is suitable for detecting fast-moving targets;

[0021] The ambiguity functions corresponding to the above single-frequency pulse signal, linear frequency modulation signal, and two-tone frequency modulation signal can also be used in combination.

[0022] Further, when continuously in the same range cell during multiple detections within the target detection range, aggregate the multiple pulses obtained by detection by adding the range cells at the same position to obtain a higher signal-to-noise ratio.

[0023] Furthermore, in step 2.2, a linear time-invariant filter is used to filter the information of different angle dimensions after the rewriting form, and the signals of different angle dimensions are expressed as follows after filtering:

[0024]

[0025] Among them, x(t) is the transmitted signal part in the reception, y x (t) and y n (t) are the results after filtering x(t) and n(t), respectively, and * indicates the calculation using convolution operation.

[0026] Furthermore, in step 2.3, the filtered information of different angle dimensions is subjected to inverse Fourier transform, and after inverse Fourier transform, it is expressed as:

[0027]

[0028] Among them, H(ω) represents the frequency domain of the matched filter.

[0029] Furthermore, in step 2.4, a suitable matched filter frequency domain is used to obtain the maximum signal-to-noise ratio, wherein the maximum signal-to-noise ratio It is expressed as,

[0030]

[0031] Among them, N 0 / 2 means the noise is Gaussian white noise with double-sided power spectral density.

[0032] Furthermore, in step 2.5, the gain of the signal-to-noise ratio before and after the matched filtering is calculated in combination with the maximum signal-to-noise ratio. The gain of the signal-to-noise ratio before and after the matched filtering is G SNR It is expressed as,

[0033]

[0034] Where T is the signal duration, B is the bandwidth of the system, It is expressed as the matched filter frequency domain to obtain the maximum signal-to-noise ratio, P x Expressed as the average power of the signal.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a target detection and distance-Doppler parameter estimation method based on matched filtering, by which a sonar system generates multiple beams within the detection range to obtain information of different angle dimensions, and the information of different angle dimensions is filtered using a matched filter, and a transmission waveform and a corresponding ambiguity function are reasonably selected to match the distance-Doppler parameters. Specifically, array processing gain and beamforming are used to obtain information of different angles, and matched filtering is performed on the received signal, so that it can improve the target detection signal-to-noise ratio while also being able to obtain parameters such as the distance and radial velocity of the target. Combined with the different recognition capabilities of different transmission signals for these parameters, the transmission waveform is reasonably selected to match them according to the ambiguity function, so that the present invention can effectively improve the distance resolution and signal-to-noise ratio. In addition, the combined waveform multi-pulse processing is combined to perform target distance-Doppler joint estimation, which can further improve the reliability of target detection and further enhance the signal-to-noise ratio at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0037] In the attached picture:

[0038] Figure 1 is a flowchart of the main steps in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of multi-beam sonar imaging in an embodiment of the present invention;

[0040] Figure 3 is a diagram of a broadband ambiguity function of a single-frequency pulse in an embodiment of the present invention;

[0041] Figure 4 is a broadband ambiguity function diagram of a linear frequency modulation signal in an embodiment of the present invention;

[0042] Figure 5 Graph showing the broadband ambiguity function of a non-hyperbolic frequency modulated pulse in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0044] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0045] The present invention discloses a target detection and range-Doppler parameter estimation method based on matched filtering, which will be described in detail below with reference to the accompanying drawings and in combination with embodiments. Figure 1 As shown, the specific steps include:

[0046] Step 1, the sonar system generates multiple beams within the target detection range to obtain information in different angle dimensions;

[0047] Step 2, filtering the information of different angle dimensions using a matched filter;

[0048] Step 3: According to the ambiguity function, the transmitted waveform signal is reasonably selected to match the range-Doppler parameters.

[0049] Specifically, in step 1, the sonar system generates multiple beams within the detection range to obtain information of different angle dimensions, such as Figure 2 As shown. The sonar system generates multiple beams in a fan-shaped area to obtain information in the angle dimension. In a single beam, the system uses the different arrival delays of target echo signals at different distances to obtain information in the distance dimension. By combining the results in the two dimensions of angle and distance, image information within the detection range can be obtained. When working, it is necessary to first drive the transmitting transducer with a pre-designed transmitting signal, collect the multi-channel echo signals received by the array, and obtain results at different angles through the beamforming algorithm.

[0050] The signal obtained by the transducer array in the integrated sonar system array can improve the signal-to-noise ratio of the received signal. When an array containing two identical omnidirectional transducers is used to receive a narrowband signal s(t), due to the different positions of the transducers, the received signal can be expressed as

[0051] y 1 (t) = As (t) + n 1 (t)

[0052] y 2 (t) = As(t)e jφ +n 2 (t), Formula 1

[0053] Among them, A is the amplitude change caused by signal propagation, and φ is the phase difference introduced by the difference in position between array elements. For simplicity, it is assumed that the amplitudes of signals received by different transducers are the same, and n 1 (t) and n2 (t) is uncorrelated noise. 1 (t) and y 2 (t) weighted addition to obtain an estimate of the received signal s(t) Expressed as

[0054]

[0055] Among them, w 1 and w 2 is the corresponding weight. In formula 1, the noise power is P n In the case of 1 (t) and y 2 The signal-to-noise ratio of (t) can be expressed as

[0056]

[0057] Where P s is the power of the signal s(t). The signal-to-noise ratio (SNR) after weighted addition can be calculated using formula 3: a Expressed as

[0058]

[0059] The signal-to-noise ratio gain SNR obtained by weighted summation a / SNR e Expressed as

[0060]

[0061] Observing formula 5, we can find that when w 2 =w 1 e -jφ When , the maximum value of Formula 5 is 2. This shows that by using an array consisting of two array elements, the maximum signal-to-noise ratio can be improved by a factor of two. In other words, when the total number of array elements in the array is N, the maximum signal-to-noise ratio gain is also N.

[0062] In step 2, information of different angle dimensions is filtered using a matched filter. The matched filter improves the signal-to-noise ratio by flipping the conjugate time domain of the transmitted signal and convolving it with the received signal. Specifically, the following steps are included:

[0063] Step 2.1, rewrite the signals of different angle dimensions into the form of the sum of the transmitted signal and the noise;

[0064] Step 2.2, using a linear time-invariant filter to filter the information of different angle dimensions after rewriting;

[0065] Step 2.3, performing inverse Fourier transform on the filtered information of different angle dimensions;

[0066] Step 2.4, using a suitable matched filter in the frequency domain to obtain the maximum signal-to-noise ratio;

[0067] Step 2.5, calculate the gain of the signal-to-noise ratio before and after matched filtering in combination with the maximum signal-to-noise ratio.

[0068] Specifically, the received signals of different angular dimensions (i.e., Formula 1) are rewritten as the sum of the transmitted signal and the noise, expressed as

[0069] s(t)=x(t)+w(t), Formula 6

[0070] Where x(t) is the transmitted signal part in the reception. A linear time-invariant filter with an impulse response of h(t) is used to filter the received signal s(t)

[0071] After filtering, the filtered signal y(t) is expressed as

[0072]

[0073] In the formula, y x (t) and y n (t) are the results of filtering x(t) and n(t), respectively, and * indicates the convolution operation. The purpose of matched filtering is to maximize the signal-to-noise ratio in the output, or to find the presence of the signal component x(t) in the received noisy signal s(t). If the signal appears at t 0 time point, then compared to y n (t 0 ), the matched filter should make y x (t 0 ) has a very large instantaneous power. Under the condition that the length of the signal x(t) is finite, it can be expressed by inverse Fourier transform as

[0074]

[0075] Since the convolution in the time domain and the product in the frequency domain are a pair of Fourier transforms, y x (t) can also be expressed by inverse Fourier transform as

[0076]

[0077] According to the Wiener-Hinchin function theorem, the average power of the noise after filtering can be expressed as

[0078]

[0079] Where W(ω) is the power spectrum density of the noise. At this time, the output of the matched filter is0 The signal-to-noise ratio at can be expressed as

[0080]

[0081] At this time, we need to find a suitable H(ω) to maximize the signal-to-noise ratio in formula 11. According to the Cauchy-Schwarz inequality, the signal-to-noise ratio is expressed as

[0082]

[0083] When the middle sign in formula 12 holds, H(ω) satisfies the following conditions and is expressed as

[0084] H(ω)=cexp(-jωt 0 )X * (ω) / N(ω), Formula 13

[0085] Where c is an arbitrary constant, and X * (ω) is the complex conjugate of X(ω). If the noise is a two-sided power spectral density N 0 / 2 Gaussian white noise, then the maximum signal-to-noise ratio shown in formula 11 is expressed as

[0086]

[0087] At this time, the frequency domain representation of the matched filter H(ω) can be simplified to

[0088] H(ω)=exp(-jωt 0 )X * (ω), Formula 15

[0089] If the average power of the signal is expressed as P x , the signal duration is represented as T, and the bandwidth of the system is written as B. Then according to formula 14, the gain of the signal-to-noise ratio before and after the matched filtering is G SNR Expressed as

[0090]

[0091] It can be found that when using a matched filter, using a larger signal bandwidth or a longer pulse length can achieve a signal-to-noise ratio gain through matched filtering. In addition to enhancing the signal-to-noise ratio during target detection, matched filtering technology can also achieve target range-Doppler parameter identification, but its performance is related to the ambiguity function of the signal.

[0092] In addition, as described in step 3, the transmission waveform and the corresponding ambiguity function are reasonably selected to match the range-Doppler parameters. By selecting a suitable transmission signal, the waveform of the matched filter output can be controlled to achieve the purpose of estimating the target range and radial velocity. The ambiguity function contains two variables, one is the speed of the target (Doppler), and the other is the distance of the target from the sonar system (delay). We define an ambiguity function χ(v, τ) expressed as χ(v, τ) = η 1 / 2 ∫x(t)x * [(t-τ)η(v)]dt. Where x(t) is the transmitted signal in formula 6, η(v) is expressed as η(v) = (1+v / c) / (1-v / c), v is the radial velocity of the target, c is the underwater sound speed, and τ is the time delay of the target. Comparing formula 15 with the ambiguity function χ(v, τ), it can be found that the ambiguity function describes the output of the matched filter between the transmitted signal and itself under different time delays and Doppler frequency offsets. Then, based on the optimal range-Doppler resolution performance, the ideal ambiguity function should be expressed as χ(v, τ) = δ(v)δ(τ), which also means that as long as the range-Doppler parameters do not match, the output of the matched filter is 0.

[0093] However, the impulse function in the ideal ambiguity function cannot be realized in practical applications. Therefore, the transmission waveform and the corresponding ambiguity function should be reasonably selected according to the application requirements. The selection rules are:

[0094] The ambiguity function corresponding to the single-frequency pulse signal is suitable for estimating the target's speed, but not for measuring the target's distance.

[0095] The ambiguity function corresponding to the linear frequency modulation signal is suitable for measuring the target distance, but not suitable for target detection;

[0096] The ambiguity function corresponding to the hyperbolic frequency modulation signal is suitable for detecting fast-moving targets;

[0097] According to actual conditions, the ambiguity functions corresponding to the above-mentioned single-frequency pulse signal, linear frequency modulation signal and hyperbolic frequency modulation signal can be used in combination.

[0098] Furthermore, the following introduces the characteristics of three commonly used signals and their ambiguity functions in active sonar systems, including single-frequency pulses, linear frequency modulation signals, and hyperbolic frequency modulation signals. A single-frequency pulse with a length of T can be expressed as

[0099] x CW (t) = cos(2πf c t+φ 0 ), t∈[0,T], Formula 17

[0100] Where f c is the transmitted signal frequency, and φ0 is the initial phase. Figure 3 As shown in Figure 1, the ambiguity function of a single-frequency pulse with a pulse length of 0.25s and a frequency of 5kHz. Figure 3 It can be found that the single-frequency signal is very sensitive to Doppler and is therefore suitable for estimating the speed of the target. However, the amplitude hardly changes under different delays, so it is not suitable for estimating the distance of the target. The linear frequency modulation signal with a length of T and a bandwidth of B can be expressed as

[0101] x LFM (t) = cos[2π(f c +k l t)t+φ 0 ], t∈[0,T], Formula 18

[0102] Where k = B / 2T, φ 0 is the initial phase, such as Figure 4 shown. Figure 4 Shows the broadband ambiguity function for a linear FM signal with a pulse length of 0.25s, a center frequency of 5kHz, and a bandwidth of 1.6kHz. Figure 4 It can be found that compared with single-frequency pulses, linear frequency modulation signals can better estimate time delays and are suitable for measuring target distances. However, under the influence of Doppler, the peak ridge of the ambiguity function widens and the amplitude decreases, which affects the performance of target detection. A hyperbolic frequency modulation signal with a length of T and a bandwidth of B can be expressed as

[0103]

[0104] Where k h =-B / [f 1 (f 1 +B)T],f 1 is the starting frequency of the signal, such as Figure 5 shown. Figure 5 The broadband ambiguity function of a hyperbolic FM signal with a pulse length of 0.25s, a center frequency of 5kHz, and a bandwidth of 1.6kHz is shown. By comparing it with a linear FM signal, it can be found that the amplitude of the modulus function of the hyperbolic FM signal is basically not affected by Doppler, so it is very suitable for detecting fast-moving targets. However, it is precisely because of this property that the results of the hyperbolic FM signal are unreliable when estimating the target speed and distance. By comparing the ambiguity functions of different signals, it can be found that different signals have their strengths. Therefore, a single signal cannot be competent for detection, distance and Doppler estimation at the same time, and signals need to be combined in practical applications.

[0105] In order to further improve the reliability of target detection, the results obtained from multiple pulses can be combined. Pulse aggregation is a technology that combines the results obtained from multiple pulses to enhance the signal-to-noise ratio. When the target is always in the same distance unit in multiple detections, pulse aggregation can be achieved directly by adding the distance units at the same position in multiple pulses. Pulse aggregation can be divided into coherent and incoherent aggregation according to the summation method. The following briefly introduces these two methods. Sampling the received signal shown in Formula 6 can obtain

[0106] s i (n) = x(n) + w i (n), Equation 20

[0107] Where s i (n) is the echo signal of the i-th pulse, i = 1, ..., N. Assume that the echo signal from the target is Aδ(n) in multiple pulses. t ), the variance of the noise signal is σ 2 , assuming the target is located at the nth t Within the distance unit, the signal-to-noise ratio at this time is A 2 / σ 2 . Through coherent pulse aggregation, the nth t The result in distance units ∑ c for

[0108]

[0109] At this time, the signal-to-noise ratio is N 2 A 2 / (Nσ 2 ), which is N times the value before aggregation. Since incoherent aggregation only focuses on the amplitude of the signal and ignores the phase information when summing, the result after incoherent aggregation is ∑ nc for,

[0110]

[0111] At this time, the gain of the signal-to-noise ratio cannot directly obtain a closed solution, and the gain will be lower than the result of coherent aggregation. However, in practical applications, the movement of the target will cause the distance unit where it is located to change.

[0112] In order to deal with the problem that the target is located in different distance units in the multi-pulse echo, the detection effect can be improved by combining the target motion trajectory. The pre-detection tracking algorithm is one of the representatives of this method. By moving the target tracking before the detection, the results obtained by multiple pulses can be fused. In order to achieve a high detection probability, the false alarm probability will increase simultaneously. Due to the existence of false alarms, the target state obtained by a single measurement is fuzzy. All these possible states are placed in a set O. iIn, it is expressed as, Where o i，j , j = 1, ..., J i is the jth possible state of the target obtained by the ith pulse, J i is the total number of all possible states. If the target's true state is z in the i-th pulse result i , then the problem can be transformed into further obtaining the target most likely state sequence Z from all possible states O obtained by multi-pulses, which are defined as O = {O 1 , O 2 , …, O N} and Z = [z 1 , z 2 , …, z N ]. At this point, the mathematical problem to be solved is transformed into the following maximum likelihood estimation problem: Using the Bayesian formula, we can get that the problem to be optimized in P(Z|O)∝P(Z,O) can be transformed into:

[0113]

[0114] Based on the first-order Markov assumption, we can write an analytical expression for the joint probability density. First, we define the observation and transition probabilities. The observation probability E i (j) is E i (j) = P{O i |z i =o i，j}=P{o i，j |z i =o i，j}, where the pulse number i = 1, ..., N, and the observation number j = 1, ..., J i . Further, the transition probability T(p,q) is defined as T(p,q)=P{z i+1 =o i+1,q |z i =o i，p}where q=1,…,J i+1 , and p = 1, ..., J i+1 . At this time, the joint probability density can be expressed as,

[0115]

[0116] It can be seen from formula 24 that the results of multiple pulses are combined together in a probabilistic manner, which enhances the performance of target detection and recognition.

[0117] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A target detection and range-Doppler parameter estimation method based on matched filtering, characterized in that: The steps include: Step 1, the sonar system generates multiple beams within the target detection range to obtain information in different angle dimensions; Step 2, filtering the information of different angle dimensions using a matched filter; Step 3: According to the ambiguity function, the transmitted waveform signal is reasonably selected to match the range-Doppler parameters.

2. The method for target detection and range-Doppler parameter estimation based on matched filtering according to claim 1, characterized in that: In step 2, the obtained information of different angle dimensions is filtered using a matched filter, which specifically includes the following steps: Step 2.1, rewriting the signals of different angle dimensions into the form of the sum of the transmitted signal and the noise; Step 2.2, using a linear time-invariant filter to filter the information of different angle dimensions after rewriting; Step 2.3, performing inverse Fourier transform on the filtered information of different angle dimensions; Step 2.4, using a suitable matched filter in the frequency domain to obtain the maximum signal-to-noise ratio; Step 2.5, calculate the gain of the signal-to-noise ratio before and after matched filtering in combination with the maximum signal-to-noise ratio.

3. The target detection and range-Doppler parameter estimation method based on matched filtering according to claim 2, characterized in that: In step 3, the transmission waveform and the corresponding ambiguity function are reasonably selected to match the range-Doppler parameters, and the selection rule is: The ambiguity function corresponding to the single-frequency pulse signal is suitable for estimating the target's speed, but not for measuring the target's distance. The ambiguity function corresponding to the linear frequency modulation signal is suitable for measuring the target distance, but not suitable for target detection; The ambiguity function corresponding to the hyperbolic frequency modulation signal is suitable for detecting fast-moving targets; The ambiguity functions corresponding to the above-mentioned single-frequency pulse signal, linear frequency modulation signal and hyperbolic frequency modulation signal can also be used in combination.

4. The method for target detection and range-Doppler parameter estimation based on matched filtering according to claim 3, characterized in that: When the target detection range is within the same distance unit after multiple detections, multiple pulses obtained by detection are aggregated by adding the distance units at the same position to obtain a higher signal-to-noise ratio.

5. The method for target detection and range-Doppler parameter estimation based on matched filtering according to claim 3, characterized in that: In step 2.2, a linear time-invariant filter is used to filter the information of different angle dimensions after the rewriting form. After filtering, the signals of different angle dimensions are expressed as: Among them, x(t) is the transmitted signal part in the reception, y x (t) and y n (t) are the results after filtering x(t) and n(t), respectively, and * indicates the calculation using convolution operation.

6. The method for target detection and range-Doppler parameter estimation based on matched filtering according to claim 3, characterized in that: In step 2.3, the filtered information of different angle dimensions is subjected to inverse Fourier transform, and after inverse Fourier transform, it is expressed as: Among them, H(ω) represents the frequency domain of the matched filter.

7. The method for target detection and range-Doppler parameter estimation based on matched filtering according to claim 3, characterized in that: In step 2.4, a suitable matched filter frequency domain is used to obtain the maximum signal-to-noise ratio. It is expressed as, Where N0 / 2 means that the noise is Gaussian white noise with a bilateral power spectral density.

8. The method for target detection and range-Doppler parameter estimation based on matched filtering according to claim 3, characterized in that: In step 2.5, the gain of the signal-to-noise ratio before and after the matched filtering is calculated in combination with the maximum signal-to-noise ratio. The gain of the signal-to-noise ratio before and after the matched filtering is G SNR It is expressed as, Where T is the signal duration, B is the bandwidth of the system, It is expressed as the matched filter frequency domain to obtain the maximum signal-to-noise ratio, P x Expressed as the average power of the signal.

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