CN116449320A - Long-time accumulation and parameter estimation method under frequency agile radar system - Google Patents
Long-time accumulation and parameter estimation method under frequency agile radar system Download PDFInfo
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Abstract
The invention relates to a long-time accumulation and parameter estimation method under a frequency agile radar system, which comprises the following steps: down-conversion and pulse compression of radar echo signals; initializing a search parameter interval and a search interval; the non-uniform frequency domain generalized Radon Fourier transform is adopted to complete the distance correction, doppler phase compensation and jump phase elimination under specific search parameters, and the coherent accumulation is completed; and traversing the search parameters, projecting the signal energy to a search parameter domain, then carrying out constant false alarm detection, and if a target exists, determining a target motion parameter and outputting a target motion point trace according to the position of the energy peak. The invention effectively improves the echo signal-to-noise ratio of the complex maneuvering target and has the target detection capability under the background of strong noise.
Description
Technical Field
The invention relates to a long-time accumulation and parameter estimation method under a frequency agile radar system.
Background
The frequency agile signal has strong agility and anti-interference capability due to low detection and interception probability, and is widely applied in the military field. The signal synthesis bandwidth is large, the distance resolution can be effectively improved, doppler blurring is eliminated, and the data rate is improved. Common sparse recovery techniques such as compressed sensing, iterative mesh optimization, etc. can be used to process frequency agile signals, extract target doppler information, and achieve accumulation. It should be noted that these techniques perform well only in high signal-to-noise ratio (SNR) scenarios, where the detection performance is significantly degraded.
With the rapid development of modern aircrafts such as stealth targets, anti-radiation missiles, near space aircrafts and the like in recent years, the traditional detection algorithm fails due to the fact that the accumulated SNR cannot be effectively improved due to high maneuverability and low detectability. For the searching and detection of the above targets, the SNR is too low as a main factor restricting the radar detection performance. New system radars generally utilize signal processing methods such as digital beam forming to increase the residence time of the target within the radar beam, and increase the accumulation duration to increase the echo energy and thus the SNR. However, long-term accumulation mainly faces the following two major problems: the high speed of the target and the increase in radar range resolution will result in an echo pulse envelope spanning multiple range bins, known as ranges
A walking effect; since the high mobility or complex environment of the target causes the target echo to produce a high-order phase, the doppler frequency therefore has a time-varying characteristic, which will cause the echo to broaden and shift in the doppler dimension, known as the doppler walk effect. These effects will severely reduce the echo accumulation gain.
In order to solve the above problem, the existing mainstream algorithms mainly include generalized Radon fourier transform (generalized Radon Fourier transform, GRFT), radon Lv Fenbu (Radon Lv's distribution, RLVD), radon fractional order fuzzy function (Radon fractional ambiguity function, RFrAF), radon linear canonical transform (Radon linear canonical transform, RLCT), and the like. The method compensates distance walking and Doppler walking in accumulation time simultaneously in a multi-dimensional grid searching mode, and projects accumulated echo energy into a multi-dimensional parameter space constructed by search parameters to form a focusing peak, so that coherent accumulation and parameter estimation are realized. The algorithm has no nonlinear processing process in the processing process, so that no SNR loss exists, and the algorithm is excellent in a low SNR scene. However, this type of approach is effective if the echo pulses are coherent, and the coherent nature of the echo pulses is destroyed because the carrier frequency of the frequency agile radar is non-constant between pulses. The random bias of the carrier frequency will generate a phase jump effect, so that the method cannot completely realize the coherent accumulation of pulse energy, and further the target detection performance is drastically reduced.
In summary, eliminating the effects of distance walk, doppler walk and phase jump of high-speed maneuvering weak target detection under the frequency agile radar system is a key for realizing long-time accumulation, and needs to be solved.
Disclosure of Invention
The invention aims to provide a long-time accumulation and parameter estimation method under a frequency agile radar system, which can solve the problems of distance walk, doppler walk and phase jump generated in the accumulation process of a high-speed maneuvering weak target under the frequency agile radar system, so as to improve the accumulation gain of echo signals and the detection performance of the radar.
In order to achieve the above objective, the present invention provides a long-time accumulation and parameter estimation algorithm, which comprises the following steps:
s1: acquiring radar echo data of a moving target, discretizing, performing down-conversion and pulse compression processing to obtain a fast-slow two-dimensional data matrix;
s2: determining the parameter search dimension, search range, search interval and search sequence length of the NUFD-GRFT according to the target motion state and the actual detection requirement;
s3: transforming the time domain pulse pressure signal to a distance frequency domain, constructing a phase compensation function corresponding to a search parameter, and carrying out NUFD-GRFT on the signal to compensate distance walking, doppler walking and phase jump at the same time so as to finish coherent accumulation of echo signals;
s4: traversing all search parameters, constructing a NUFD-GRFT domain detection unit diagram, carrying out constant false alarm detection on the NUFD-GRFT domain detection unit diagram, and judging whether a target exists or not.
S5: if the target is judged to exist, determining a target motion parameter estimated value according to the position of the peak value of the target, and taking a corresponding search curve as a motion point trace of the target to estimate.
In a long-time accumulation and parameter estimation algorithm based on NUFD-GRFT under a frequency agility system of a preferred example, the method comprises the following steps:
s1: at the receiving end of the coherent radar, the radar receives the radar echo data which is amplified and limited, and samples the radar echo data along the fast time and the slow time. Typically, the range-wise sampling time interval is equal to the radar range resolution unit and the slow time dimension sampling frequency is equal to the pulse repetition frequency. The radar performs down-conversion and pulse compression processing on the baseband echo signal obtained by completing sampling after receiving, and a fast-slow two-dimensional pulse pressure signal form containing target information is obtained as follows:
s2: and determining the search dimension, the search range, the search interval and the search sequence length of the target parameters according to the maneuvering characteristics and the actual detection requirements of the target. Specifically, the initial radial distance search range is [ r min ,r max ]The search range needs to cover the area where the initial position of the target is located. The search interval is the size of the resolution of the radar range bin, i.e. Δr=c/2B. The initial radial distance search sequence length isThe radial velocity search range is [ v ] according to the expected target motion state min ,v max ]The search interval is the size of the radar doppler cell resolution, i.e. Δv=λ/2T. The radial velocity search sequence length is +.>The radial first-order acceleration search range is [ a ] 1,min ,a 1,max ]The search interval is deltaa 1 ＝λ/2T 2 The radial first-order acceleration search sequence has a length of +.>The radial second-order acceleration search range is [ a ] 2,min ,a 2,max ]The search interval is deltaa 2 ＝λ/2T 3 The length of the radial second-order acceleration search sequence is +.>
S3: performing FFT on equation (16) along the fast time to obtain the frequency domain pulse pressure signal:
next, the following phase compensation function is constructed:
then, the compensation function (18) is multiplied by the two-dimensional pulse pressure data matrix formula (17) to obtain the following steps:
IFFT is performed on the above edge fast time to restore the frequency domain signal to the time domain:
g r (t m ,τ)＝IFFT f [G r (t m ,f)](20)，
and finally, accumulating accumulated pulse train energy along a slow time domain, and projecting amplitude values in a distance unit where echo pulse energy is located to a corresponding search parameter domain, so that NUFD-GRFT can be completed once:
s4: traversing all search parameters to obtain echo energy accumulation amplitude values under all search parameters, and constructing and completing a four-dimensional NUFD-GRFT search parameter space R (R) i ,v j ,a 1,p ,a 2,q ). Its amplitude is taken as the detection statisticAnd comparing with an adaptive detection threshold for a given false alarm probability:
wherein eta is the detection threshold. If the amplitude of the detection unit exceeds the threshold, judging that a target exists; if the amplitude of the detection unit is lower than the threshold, judging that no target exists, and continuing to process the subsequent detection unit.
S5: if a target exists in the judging unit diagram, according to the peak coordinates corresponding to the NUFD-GRFT domain detection unit where the target exists, the estimated value of the target parameter can be obtained as follows:
the resulting estimate of the target motion trajectory can then be expressed as:
the invention also provides a computer device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for long-time accumulation and parameter estimation detection of the weak target in high-speed maneuver.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the high-speed maneuver weak target long-time accumulation and parameter estimation detection method of any one of the above.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the invention provides a long-time accumulation and parameter estimation algorithm under a frequency agile radar system, computer equipment and a computer readable storage medium. Different from the detection method under the conventional pulse-Doppler system, the invention provides the detection method of the non-uniform frequency domain generalized Radon Fourier transform target, solves the problem of phase jump caused by random bias of carrier frequency in the accumulation process, and effectively improves the accumulation SNR.
2. The long-time accumulation and parameter estimation method under the frequency agile radar system provided by the invention effectively solves the problems of distance walk and Doppler walk effects generated in the long-time coherent accumulation process of a weak target moving at a high speed, greatly improves the accumulation gain of echo signals, and further improves the radar detection performance.
3. The long-time accumulation and parameter estimation method under the frequency agile radar system provided by the invention adopts a multidimensional joint search mode to complete the coherent accumulation and obtain the target motion parameters. There is no nonlinear operation during the process, so the SNR loss is almost negligible. Therefore, the algorithm provided by the invention has strong robustness for detecting the weak target in a low SNR scene.
4. The method is different from the conventional Radon processing method, the time domain pulse pressure signal is converted into the frequency domain for processing, and the Doppler matched filtering is realized by constructing a phase compensation function in the frequency domain and multiplying the phase compensation function by the original signal frequency domain in a conjugate way. The processing mode eliminates the pulse offset quantization error caused by the cyclic shift and addressing operation adopted by the time domain processing, so the method provided by the invention improves the estimation accuracy.
Drawings
FIG. 1 is a step diagram of a method for long-term accumulation and parameter estimation in a frequency agile radar system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process flow of a method for long-term accumulation and parameter estimation under a frequency agile radar system according to an embodiment of the present invention;
FIG. 3 is a block diagram illustrating a long-term accumulation and parameter estimation method under a frequency agile radar system according to an embodiment of the present invention;
FIG. 4 (a) shows the pulse pressure of echo signals;
FIG. 4 (b) is a cross-sectional view showing the accumulation result of the radial velocity and the first-order acceleration;
FIG. 4 (c) is a cross-sectional view showing the accumulation results of the first-order acceleration and the second-order acceleration;
fig. 4 (d) is a cross-sectional view of the initial radial distance accumulation result.
Detailed Description
The invention relates to a technology for carrying out envelope alignment and phase compensation and eliminating phase jump on radar echo signals under the conditions of span unit, doppler unit and phase jump, and completing target echo energy coherent accumulation and target motion parameter estimation.
The present invention will be described in more detail below with reference to the accompanying drawings showing embodiments of the invention. The invention may, however, be embodied in different forms, specifications, and the like and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, relative dimensions may be exaggerated or reduced for clarity.
As shown in fig. 1, the embodiment provides a long-time accumulation and parameter estimation algorithm under the frequency agile radar system. The specific implementation steps are as follows:
s1: and acquiring moving target radar echo data, discretizing, and performing down-conversion and pulse compression.
The coherent radar transmits a transmission signal for observation and receives the returned echo data. Sampling M groups of echo data to be accumulated, discretizing the sampled data, and extracting a target observation value S in a fast-slow time two-dimensional plane r (t m τ). And performing down-conversion and pulse compression processing on the discretized radar echo data to obtain a two-dimensional pulse pressure matrix for coherent accumulation detection.
Specifically, the frequency agile radar transmits a set of chirp signals:
s t (t m ,τ)＝rect(τ/T p )exp(jπγτ 2 )exp[j2π(f c +χ(n)Δf)(t m +τ)] (25)，
in the middle ofRect () represents a gate function, γ=b/T p Represents the modulation frequency, B is the signal bandwidth, T p Representing pulse duration, f c X (n) is a random sequence and satisfies the range [ -alpha, +alpha]Wherein 0 < alpha.ltoreq.0.5, Δf is the frequency interval, τ represents the fast time, i.e. the distance time, t m ＝mT r M=0, 1,..m-1 represents slow time, T r The pulse repetition period is represented, and M represents the number of accumulated pulses.
Assuming that there is a single target in the radar detection area, the baseband echo signal received by the radar in a coherent processing interval is as follows:
wherein A is 0 The complex amplitude value, c, is the speed of light.
Wherein r (t) m ) The specific form of the instantaneous radial distance of the target relative to the radar is as follows:
wherein r is 0 For the initial radial distance of the target, v 0 For a target radial velocity, a 1 For the target radial first order acceleration, a 2 Is the target radial second-order acceleration.
Performing down-conversion and pulse compression processing on the baseband echo signal to obtain a fast-slow two-dimensional pulse pressure signal containing target information, wherein the fast-slow two-dimensional pulse pressure signal comprises the following forms:
wherein A is 1 Is the complex amplitude of the signal.
S2: initializing search parameters, and determining search parameter dimensions, search ranges, search intervals and discretized search sequences.
S2-1: determining an initial radial distanceThe search range of the radial velocity, the radial first-order acceleration and the radial second-order acceleration is [ r ] min ,r max ]、[v min ,v max ]、[a 1,min ,a 1,max ]And [ a ] 2,min ,a 2,max ]Wherein r is min And r max Representing the minimum search distance and the maximum search distance, v, respectively min And v max Respectively representing a minimum search speed and a maximum search speed, a 1,min And a 1,max Respectively representing a minimum search first-order acceleration and a maximum search first-order acceleration, a 2,min And a 2,max Representing the minimum search second order acceleration and the maximum search second order acceleration, respectively. It should be noted that the search parameter range may be determined based on radar observation data, and some prior information and motion characteristics of the target;
s2-2: the search intervals for determining the initial radial distance, radial velocity, radial first-order acceleration and radial second-order acceleration are denoted as Δr=c/2B, Δv=λ2t, Δa, respectively 1 ＝λ/2T 2 And delta a 2 ＝λ/2T 3 Where λ=c/f c The signal wavelength is T, and T is a coherent processing interval;
s2-3: determining the initial radial distance, radial velocity, radial first-order acceleration, and radial second-order acceleration search sequence lengths are expressed as And->Wherein->Representing an upward rounding function;
s2-4: determining a discretized initial radial distance, a radial velocity, a radial first-order acceleration and a radial second-order acceleration search sequence, which are respectively expressed as:
r i ＝r min +iΔr,i＝0,1,...,N r -1 (29)，
v j ＝v min +jΔv,j＝0,1,...,N v -1 (30)，
thereby completing the search of the parameter (r i ,v j ,a 1,p ,a 2,q ) Is set up in the initialization of the system.
S3: and performing NUFD-GRFT on the signals to complete distance correction, doppler correction and phase jump elimination, and realizing coherent accumulation of echo signals.
Specifically, step S3 further includes:
s3-1: taking an FFT of equation (28) along the fast time, the frequency domain pulse pressure signal can be expressed as:
wherein A is 2 For complex amplitude values, f is the frequency corresponding to the fast time, i.e. the distance frequency.
S3-2: constructing a conjugate phase compensation function of an exponential term causing distance walk and Doppler walk in a fast time frequency domain:
wherein r is i ∈[r min ,r max ]，i＝1,2,…,N r ；v j ∈[v min ,v max ]，j＝1,2,…,N v ；a 1,p ∈[a 1,min ,a 1,max ]，p＝1,2,…,N a1 ；a 2,q ∈[a 2,min ,a 2,max ]，q＝1,2,…,N 2,q 。
S3-3: multiplying the compensation function (34) with the fast time frequency-slow time two-dimensional pulse pressure data matrix (33) yields:
s3-4: IFFT is performed on the above edge fast time to restore the frequency domain signal to the time domain:
g r (t m ,τ)＝IFFT f [G r (t m ,f)] (36)，
s3-5: accumulating accumulated pulse train energy along a slow time domain, and projecting amplitude values in a distance unit where echo pulse energy is located to a corresponding search parameter domain to complete NUFD-GRFT once:
specifically, the frequency domain discrete form of NUFD-GRFT can be expressed as:
wherein m and n are index values of slow time and fast time frequency sequences, respectively. F (F) r ＝f s L is a fast time frequency resolution unit, f s For fast time sampling frequency, L is the fast time dimension sampling point number.
Applying an IFFT to equation (38), transforming the compensated pulse pressure signal to the time domain, and summing the accumulated bursts in phase, a discrete form of this process can be expressed as:
where k is the fast time series index corresponding to n.
S4: and traversing all the search parameters, and then performing CFAR detection on the output result to judge whether the target exists or not.
Traversing all search parameters (r i ,v j ,a 1,p ,a 2,q ) The steps S3-1 to S3-5 are repeated to obtain the echo energy accumulation amplitude values under all the search parameters, and a four-dimensional NUFD-GRFT search parameter space R (R) can be constructed i ,v j ,a 1,p ,a 2,q ). The accumulated amplitude is used as detection statistic and compared with an adaptive detection threshold under the given false alarm probability:
wherein eta is the detection threshold determined by the given false alarm probability and the reference units around the detection unit, H 1 Corresponding to the existence of target, H 0 The corresponding target does not exist. If the amplitude of the detection unit exceeds the threshold, judging that a target exists; if the amplitude of the detection unit is lower than the threshold, judging that no target exists, and continuing to process the subsequent detection unit.
S5: based on the above analysis, if the CFAR detection determines that there is a target, and when searching for the parameter r i ＝r 0 ,v j ＝v 0 ,a 1,p ＝a 1 ,a 2,q ＝a 2 The constructed frequency domain phase compensation function H (t m F) can accurately compensate and eliminate distance walk, doppler walk and phase jump caused by the phase term of the formula (33). Thus, the corresponding NUFD-GRFT output under this condition is:
from the above equation, when the search parameter matches the target real parameter, the echo energy can be completely extracted and accumulated, and the accumulated output reaches the maximum value. Therefore, according to the peak coordinates corresponding to the NUFD-GRFT domain detection unit where the target is located, the estimated value of the target parameter can be obtained as follows:
further, a target motion trace corresponding to the expression (27) is obtained:
FIG. 2 shows a process flow diagram of a NUFD-GRFT detection method.
Fig. 3 shows a full flow block diagram of the long-time accumulation and parameter estimation method provided by the invention.
As shown in fig. 4 (a) to 4 (d), the performance of the high-speed maneuvering weak target long-time accumulation and parameter estimation method provided by the invention is also verified by combining simulation tests. The target motion parameter is set as r 0 ＝50km,v＝1700m/s,a 1 ＝20m/s 2 ,a 2 ＝10m/s 3 . The simulation result shows that the method provided by the invention can effectively detect the target and accurately estimate the motion parameters thereof.
In particular, the invention also provides a computer device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for long-time accumulation and parameter estimation detection of the weak target in high-speed maneuver.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the high-speed maneuver weak target long-time accumulation and parameter estimation detection method of any one of the above.
Those skilled in the art will appreciate that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a non-volatile computer readable storage medium, and the computer program may include the above-described embodiment of the high-speed high-mobility weak target long-term accumulation and parameter estimation method when executed, and will not be repeated herein.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The long-time accumulation and parameter estimation method under the frequency agile radar system is characterized by comprising the following steps of:
step S1, radar echo data of a moving target are obtained and discretized, and down-conversion and pulse compression processing are carried out to obtain a fast-time and slow-time two-dimensional pulse pressure data matrix;
step S2, establishing an instantaneous radial distance equation between a target and a radar according to radar system parameters and target motion types, and determining a target motion parameter search interval and a target motion parameter search interval of expected compensation according to actual engineering index requirements;
s3, after the time domain pulse pressure signal is transformed to a distance frequency domain, a frequency domain phase compensation function is constructed according to specific searching initial distance, searching speed, searching first-order acceleration and searching second-order acceleration, and after the compensation function is multiplied with the frequency domain pulse pressure signal, inverse Fourier transformation is carried out on the signal; after the signals are transformed to the time domain, pulse energy data in a distance and Doppler two-dimensional data matrix are extracted and accumulated, and echo energy after coherent accumulation is obtained;
step S4, traversing all initial radial distance, speed, first-order acceleration and second-order acceleration search parameters of the targets, repeating the step S3, constructing accumulated energy amplitude values under different search parameters into a NUFD-GRFT domain detection unit diagram, and performing constant false alarm detection on the NUFD-GRFT domain detection unit diagram to judge whether the targets exist or not;
step S5, if the target is judged to exist, the target motion parameter estimation is completed according to the peak position coordinates corresponding to the NUFD-GRFT domain detection unit where the target exists; meanwhile, the corresponding search curve is used as a third-order motion point trace equation of the high-speed maneuvering target:
wherein r (t) m ) At t m Instantaneous radial distance between radar and target at moment, r 0 For the initial radial distance of the target, v 0 For a target radial velocity, a 1 For the target radial first order acceleration, a 2 The target radial second-order acceleration;
t m ＝mT r m=0, 1,..m-1 is a slow time series, T r For the pulse repetition interval, M is the number of accumulated pulses.
2. The method for long-term accumulation and parameter estimation under a rate agile radar system according to claim 1, wherein in step S1, if a transmitted signal chirp signal is set, a time domain signal after pulse compression is completed may be expressed as:
wherein A is 1 Is the target backscattering coefficient, c is the speed of light, B is the signal bandwidth, χ (n) is the random sequence, and satisfies the range [ -alpha, +alpha]Is the frequency spacing, f c Is the reference frequency for the carrier frequency.
3. The method for long-term accumulation and parameter estimation under a rate agile radar system according to claim 1, wherein step S2 includes:
s2-1, determining the search range of the initial radial distance, the radial speed, the radial first-order acceleration and the radial second-order acceleration as [ r ] min ,r max ]、[v min ,v max ]、[a 1,min ,a 1,max ]And [ a ] 2,min ,a 2,max ]Wherein r is min And r max Representing the minimum search distance and the maximum search distance, v, respectively min And v max Representing minimum searches respectivelySpeed and maximum search speed, a 1,min And a 1,max Respectively representing a minimum search first-order acceleration and a maximum search first-order acceleration, a 2,min And a 2,max Respectively representing the minimum search second-order acceleration and the maximum search second-order acceleration;
s2-2, determining the search interval of initial radial distance, radial velocity, radial first-order acceleration and radial second-order acceleration, denoted as Deltar=c/2B, deltav=lambda/2T, deltaa, respectively 1 ＝λ/2T 2 And delta a 2 ＝λ/2T 3 Where λ=c/f c The signal wavelength is T, and T is a coherent processing interval;
s2-3, determining initial radial distance, radial speed, radial first-order acceleration and radial second-order acceleration search sequence lengths to be respectively expressed as And->Wherein (1)>Representing an upward rounding function;
s2-4, determining a discretized initial radial distance, a radial velocity, a radial first-order acceleration and a radial second-order acceleration search sequence, which are respectively expressed as:
r i ＝r min +iΔr,i＝0,1,...,N r -1 (3)，
v j ＝v min +jΔv,j＝0,1,...,N v -1 (4)，
thereby completing the search of the parameter (r i ,v j ,a 1,p ,a 2,q ) Is set up in the initialization of the system.
4. A method for long-term accumulation and parameter estimation in a frequency agile radar system according to claim 1,
the step S3 comprises the following steps:
s3-1, FFT is performed on the fast time of the formula (2), and the frequency domain pulse pressure signal can be expressed as:
wherein A is 2 For complex amplitude values, f is the frequency corresponding to the fast time, i.e. the distance frequency.
S3-2, constructing a conjugate phase compensation function of an exponential term causing distance walk and Doppler walk in a fast time frequency domain:
wherein r is i v[r min ,r max ]，i＝1,2,…,N r ；v j ∈[v min ,v max ]，j＝1,2,…,N v ；a 1,p ∈[a 1 ,min,a 1,max ]，p＝1,2,…,N a1 ；a 2,q ∈[a 2,min ,a 2,max ]，q＝1,2,…,N 2,q ；
S3-3, multiplying the compensation function (8) by a fast time frequency-slow time two-dimensional pulse pressure data matrix formula (9) to obtain:
s3-4, performing IFFT on the fast edge time to restore the frequency domain signal to the time domain:
g r (t m ,τ)＝IFFT f [G r (t m ,f)](11)，
s3-5, accumulating accumulated pulse train energy along a slow time domain, and projecting amplitude values in a distance unit where echo pulse energy is located to a corresponding search parameter domain, so that one NUFD-GRFT can be completed:
and traversing all the search parameters to obtain echo energy accumulation amplitude values under all the search parameters, and constructing and completing a four-dimensional NUFD-GRFT search parameter space.
5. A method for long-term accumulation and parameter estimation in a frequency agile radar system according to claim 1,
and (4) performing constant false alarm detection in the step S4, wherein the expression is as follows:
wherein eta is an adaptive detection threshold, and is determined by a given false alarm probability and reference units around a detection unit, H 1 Corresponding to the existence of target, H 0 The corresponding target does not exist.
6. The method for long-term accumulation and parameter estimation under frequency agile radar system according to claim 1 and claim 4, wherein the method for estimating target motion parameters and motion points is as follows:
according to the peak coordinates corresponding to the NUFD-GRFT domain detection unit of the target, the estimated value of the target parameter can be obtained as follows:
wherein,,for the estimated value of the initial radial distance, +.>For radial velocity estimation, +.>As the radial first-order acceleration estimation value,is a radial second-order acceleration estimated value;
further, a target motion trajectory corresponding to the expression (1) is estimated:
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| CN117849753A (en) * | 2024-03-07 | 2024-04-09 | 长沙莫之比智能科技有限公司 | Target general feature extraction method based on vehicle-mounted millimeter wave radar |
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| CN117826156B (en) * | 2024-03-05 | 2024-05-28 | 西安瀚博电子科技有限公司 | Adaptive decision-based step frequency radar motion compensation method |
| CN117849753A (en) * | 2024-03-07 | 2024-04-09 | 长沙莫之比智能科技有限公司 | Target general feature extraction method based on vehicle-mounted millimeter wave radar |
| CN117849753B (en) * | 2024-03-07 | 2024-05-03 | 长沙莫之比智能科技有限公司 | Target general feature extraction method based on vehicle-mounted millimeter wave radar |
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