CN110398730B - Maneuvering target coherent detection method based on coordinate rotation and non-uniform Fourier transform - Google Patents
Maneuvering target coherent detection method based on coordinate rotation and non-uniform Fourier transform Download PDFInfo
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Abstract
The invention belongs to the technical field of radar target signal estimation, and particularly relates to a maneuvering target coherent detection method based on coordinate rotation and non-uniform Fourier transform, which comprises the following steps: aiming at radar signals, establishing a maneuvering target signal model; and performing range migration processing on the maneuvering target signal model, and estimating target motion parameters through coordinate rotation and non-uniform Fourier transform. The method can relieve the contradiction between the calculation complexity and the detection performance, eliminates QRM by utilizing second-order Keystone transform (SoKT), and constructs a phase compensation function by combining the relation between the rotation angle and the Doppler frequency; and simulation experiments verify that compared with the existing representative algorithm, the method can obtain nearly ideal detection performance with lower operation complexity and has stronger application prospect.
Description
Technical Field
The invention belongs to the technical field of radar target signal estimation, and particularly relates to a maneuvering target coherent detection method based on coordinate rotation and non-uniform Fourier transform.
Background
In recent years, with the rapid development of stealth airplanes and Unmanned Aerial Vehicles (UAVs), the radar maneuvering weak target detection is required to be higher and higher. To detect such low radar cross-sectional area (RCS) targets, long-term coherent accumulation is an indispensable tool. Unfortunately, due to the high mobility of the above targets, Linear Range Migration (LRM), Quadratic Range Migration (QRM) and Doppler Frequency Migration (DFM) are difficult to avoid for the targets within the accumulation time. These adverse factors will severely affect the detection performance of traditional accumulation algorithms (e.g. MTD). Therefore, how to robustly detect a maneuvering weak target becomes a hot topic in the field of radar signal processing.
The LRM will cause a cross-range cell migration of the target envelope. To eliminate the LRM, many successful algorithms are proposed, such as inverse variable-scale Fourier transform (SCIFT), Keystone Transform (KT), modified coordinate rotation transform (MLRT), and Radon-Fourier transform. These algorithms enable satisfactory detection capabilities with moderate computational effort. However, they suffer from severe cumulative performance loss due to neglect of QRM and DFM effects caused by target acceleration. To solve this problem, researchers have expended a great deal of effort and developed many approaches, which can roughly be divided into two categories in general: (a) search-type algorithms, representative methods include generalized Radon-Fourier transform (GRFT), KT and Lu-distribution algorithms (KT-LVD), KT and linear canonical transform (KT-LCT), modified axial rotation transform and Lu-distribution (MART-LVT), modified axial rotation and fractional Fourier transform (IAR-FRFT), and Radon-Lu-distribution (RLVD). These algorithms exhibit superior detection performance at low signal-to-noise ratios (SNR) through parameter searching. However, the great computational complexity makes it unacceptable in practical applications. (b) Non-search class algorithms, typical methods include symmetric autocorrelation function-variable scale fourier transform (SAF-SFT), three-dimensional variable scale transform (TDST), frequency second order phase difference (FD-SoPD), and Adjacent Cross Correlation Function (ACCF). These algorithms reduce the coupling order by correlation operations, greatly reducing the computational burden. However, the correlation belongs to a nonlinear operation, which causes cross term interference under multiple targets and reduces the noise immunity.
Disclosure of Invention
Therefore, the invention provides the maneuvering target coherent detection method and device based on coordinate rotation and non-uniform Fourier transform, which can relieve the contradiction between the calculation complexity and the detection performance, obtain nearly ideal detection performance with lower computation amount and have stronger application prospect.
According to the design scheme provided by the invention, the maneuvering target coherent detection method based on coordinate rotation and non-uniform Fourier transform comprises the following contents:
A) aiming at radar signals, establishing a maneuvering target signal model;
B) and performing range migration processing on the maneuvering target signal model, and estimating target motion parameters through coordinate rotation and non-uniform Fourier transform.
In the above, a) is to establish a maneuvering target signal model, which includes the following contents: assuming that the radar transmits a linear frequency modulation signal, the maneuvering target moves at constant acceleration, and the instantaneous slope distance of the target from the radar is obtained according to the slow time variable, the initial slope distance of the target, the radial speed and the acceleration; obtaining radar receiving signals according to the instantaneous slope distance, the target radial speed, the signal amplitude and the bandwidth; and carrying out Fourier transform on the radar receiving signals along a fast time axis to obtain a maneuvering target signal model.
And B) eliminating the effects of linear range migration, secondary range migration and Doppler frequency migration.
Preferably, in B), second-order Keystone transformation is adopted to eliminate second-order range migration caused by the acceleration of the maneuvering target.
Preferably, the second-order Keystone transform eliminates the second-order range migration process, and comprises the following contents: scaling the slow time of each distance frequency unit, and bringing the scaled slow time variable into a maneuvering target signal model; and simplifying and representing the maneuvering target signal model obtained after scaling through Taylor expansion, performing Fourier inverse transformation along the range frequency, completing secondary coupling between Doppler frequency and slow time variable, and correcting a secondary range migration effect caused by the maneuvering target.
Preferably, in B), the jointly estimated target motion parameters include the following: representing the maneuvering target signal model as a discrete form under a coordinate system; eliminating residual linear distance migration effect by introducing coordinate rotation transformation, extracting signal energy along the azimuth direction, and estimating the equivalent speed, the unambiguous speed and the real speed of the maneuvering target by using the rotation angle; constructing a phase compensation function according to the estimated non-fuzzy speed, and multiplying the phase compensation function by the extracted signal energy to compensate a linear phase term; and carrying out parameter estimation by utilizing non-uniform Fourier transform.
Preferably, in the parameter estimation by using the non-uniform fourier transform, the non-uniform fourier transform is performed on the multiplication result, and the maneuvering target energy accumulation is determined to be a single peak condition according to the non-uniform fourier transform, and the maneuvering target acceleration is estimated from the single peak condition.
Preferably, a phase compensation function is constructed according to the estimated target speed and acceleration; carrying out slow time Fourier transform and range frequency Fourier inverse transform on the maneuvering target signal model to complete coherent accumulation; obtaining a single spectral peak of a distance-Doppler domain after the mobile target receiving signal is accumulated; target detection is accomplished by a single spectral peak.
Preferably, the target detection is finished by using a constant false alarm aiming at a single spectrum peak value, if the peak value exceeds a self-adaptive threshold, a moving target is judged to exist, and a moving target parameter is obtained; and if the peak value is smaller than the threshold, judging that the moving target is not detected.
Furthermore, the invention also provides a coherent detection device for a maneuvering target based on coordinate rotation and non-uniform Fourier transform, which comprises: a model building module and a joint estimation module, wherein,
the model establishing module is used for establishing a maneuvering target signal model aiming at the radar signal;
and the joint estimation module is used for carrying out range migration processing on the maneuvering target signal model and estimating target motion parameters through coordinate rotation and non-uniform Fourier transform.
The invention has the beneficial effects that:
aiming at radar signals, a maneuvering target signal model is established; the method comprises the steps of carrying out range migration processing on a maneuvering target signal model, and estimating target motion parameters through coordinate rotation and non-uniform Fourier transform in a combined manner, so that the contradiction between the calculation complexity and the detection performance is effectively relieved; and second-order Keystone transform (SoKT) is utilized to eliminate QRM, and a phase compensation function is constructed by combining the relation between the rotation angle and the Doppler frequency, so that almost ideal detection performance can be obtained with lower operation complexity compared with the conventional representative algorithm. Finally, the effectiveness of the technical scheme of the invention is further verified through simulation and actual measurement radar data processing results.
Description of the drawings:
FIG. 1 is a flow chart of a coherent detection method in an embodiment;
FIG. 2 is a schematic diagram of an embodiment of a coherent detection device;
FIG. 3 is a flow chart of the LRT-NuFFT algorithm in an embodiment;
FIG. 4 is a comparison of algorithm computation complexity in the examples;
FIG. 5 is a schematic diagram of coherent accumulation simulation results of a single maneuvering target in an embodiment;
FIG. 6 is a diagram illustrating a multi-target coherent accumulation simulation result of the LRT-NuFFT algorithm in the embodiment;
FIG. 7 is a graph showing the variation of the target detection probability with SNR in the example;
fig. 8 is a schematic diagram of the processing result of the measured UAV data in the embodiment.
The specific implementation mode is as follows:
in order to make the objects, technical solutions and advantages of the present invention clearer and more obvious, the present invention is further described in detail below with reference to the accompanying drawings and technical solutions.
The long-time coherent accumulation can obviously improve the detection and motion parameter estimation capability of the radar on the maneuvering target. However, Linear Range Migration (LRM), Quadratic Range Migration (QRM), and Doppler Frequency Migration (DFM) during the coherent processing severely degrade radar detection and estimation performance. In view of this, in the embodiment of the present invention, referring to fig. 1, a method for coherent detection of a maneuvering target based on coordinate rotation and non-uniform fourier transform is provided, which includes the following steps:
s101) aiming at radar signals, establishing a maneuvering target signal model;
s102) carrying out range migration processing on the maneuvering target signal model, and jointly estimating target motion parameters through coordinate rotation and non-uniform Fourier transform.
Further, in the embodiment of the invention, assuming that the radar transmits a linear frequency modulation signal, the maneuvering target moves at a constant acceleration, and the instantaneous slope distance of the target from the radar is obtained according to the slow time variable, the initial slope distance of the target, the radial speed and the acceleration; obtaining radar receiving signals according to the instantaneous slope distance, the target radial speed, the signal amplitude and the bandwidth; and carrying out Fourier transform on the radar receiving signals along a fast time axis to obtain a maneuvering target signal model.
The radar transmits a Linear Frequency Modulated (LFM) signal,
wherein,
Tpand fcFor the pulse duration and the carrier frequency of the signal,
and gamma denotes the fast time variation and chirp rate, respectively.
The maneuvering target moves with constant acceleration, its instantaneous slope distance R (t) from the radarm) Satisfy the requirement of
Wherein R is0V and a are the initial slope, radial velocity and acceleration of the target, respectively. t is tm＝mT(m＝1,2,…,Na) Representing a slow time variable, T being the pulse repetition time, NaTo accumulate the number of pulses.
Neglecting the effects of noise, the received signal after pulse pressure can be expressed as:
wherein A iscAnd B signal amplitude and bandwidth, respectively.
The formula (2) can be substituted into the formula (3)
Wherein λ ═ c/fcIs the signal wavelength.
Doppler ambiguity often occurs due to the low pulse repetition frequency of the radar and the high velocity of the target. Thus, the radial velocity of the target may be expressed as
v＝Nbvb+v0 (5)
Wherein v isb＝λfpA/2 and fpRespectively representing radar blind speed and pulse repetition frequency. N is a radical ofbIs a target Doppler ambiguity number, v0Is free of blurring speed and satisfies-vb/2≤v0＜vb/2。
The formula (5) is inserted into the formula (4) to obtain:
in the above equation, the equation exp (-j2 π f is usedcNbvbtm/c)＝1。
It can be observed from equation (6) that the signal envelope varies non-linearly with slow time. The LRM effect occurs when the offset exceeds a distance resolution unit Δ r ═ c/2B during the integration time. The QRM effect can also be observed if the target has high mobility (i.e. possesses large acceleration). Fourier Transform (FT) is performed on equation (6) along the fast time axis, and the Fourier Transform (FT) can be obtained
Wherein f isrIs relative to
The doppler frequency of (d). Formula (7) shows thatrAnd tmThe coupling relationship between them is the root cause for LRM and QRM. The doppler frequency brought by the target is defined as:
due to the acceleration of the target, a linear Doppler shift occurs, with a frequency shift of 2atmAnd/lambda. If the value exceeds the Doppler resolution cell 1/NaT, then the DFM effect occurs and causes the doppler domain energy to diverge. Therefore, in order to detect a maneuvering target at low signal-to-noise ratio, the effects of LRM, QRM, and DFM must be effectively eliminated. Therefore, in the embodiment of the invention, the range migration processing comprises the elimination processing of the effects of linear range migration, quadratic range migration and Doppler frequency migration.
Further, the second-order Keystone transformation is adopted to eliminate the second-order range migration caused by the acceleration of the maneuvering target in the embodiment of the invention. Preferably, the second-order Keystone transform eliminates the second-order range migration process, and comprises the following contents: scaling the slow time of each distance frequency unit, and bringing the scaled slow time variable into a maneuvering target signal model; and simplifying and representing the maneuvering target signal model obtained after scaling through Taylor expansion, performing Fourier inverse transformation along the range frequency, completing secondary coupling between Doppler frequency and slow time variable, and correcting a secondary range migration effect caused by the maneuvering target.
Second order coupling caused by target acceleration is eliminated by using a second order Keystone transform (SoKT). SoKT scales the slow time of each range frequency unit, which can be expressed as
Wherein, taIs a scaled slow time variable.
The formula (9) may be substituted for the formula (7):
for narrow band radar systems, one-order taylor spreads are typically satisfied:
therefore, the formula (10) can be simplified to
Wherein, Ve＝Nbvb+v0The/2 is defined as the equivalent speed of the target.
Distance frequency f along equation (12)rBy performing an Inverse Fourier Transform (IFT), we can obtain
Wherein
The representation corresponds to
Is located at the axis of the pitch.
As can be observed from formulae (12) and (13), SoKT eliminates frAnd tmA second order coupling between them. Thus, the QRM effect caused by the target is effectively corrected.
Further, in the embodiment of the present invention, the jointly estimating the target motion parameter includes the following contents: representing the maneuvering target signal model as a discrete form under a coordinate system; eliminating residual linear distance migration effect by introducing coordinate rotation transformation, extracting signal energy along the azimuth direction, and estimating the equivalent speed, the unambiguous speed and the real speed of the maneuvering target by using the rotation angle; constructing a phase compensation function according to the estimated non-fuzzy speed, and multiplying the phase compensation function by the extracted signal energy to compensate a linear phase term; and carrying out parameter estimation by utilizing non-uniform Fourier transform. Preferably, in the parameter estimation by using the non-uniform fourier transform, the non-uniform fourier transform is performed on the multiplication result, and the maneuvering target energy accumulation is determined to be a single peak condition according to the non-uniform fourier transform, and the maneuvering target acceleration is estimated from the single peak condition.
The residual LRM and QFM in equation (13) pose a significant difficulty for coherent accumulation. The embodiment of the invention provides the method for eliminating LRM and QFM by comprehensively utilizing LRT and NuFFT. Notice ta＝mT，fsKB, where fsIs the signal sampling rate and K is the over-sampling rate. Therefore, R ═ ρ n, R0＝ρnR0Where ρ ═ c/2fsDistance sampling unit, n and nR0Are respectively R and R0The distance unit identification bit. Equation (13) can be expressed in discrete form in the (n, m) coordinate system, i.e.
In order to compensate for the LRM effect caused by the equivalent velocity, an LRT is introduced, which is defined as a coordinate rotation operation as shown below,
wherein (m ', n') is a coordinate after rotation,
is the angle of rotation.
Formula (15) may be substituted for formula (14):
when in use
Or equivalently
The LRM in equation (16) will be corrected, i.e.,
as shown in equation (17), the target energies are concentrated in the same range bin. The signal energy is then extracted in the azimuth direction. For a particular range bin, the extracted signal can be expressed as:
obviously, sazi(m') is a chirp signal. The LVT is adopted to perform coherent accumulation and motion parameter estimation, and although satisfactory accumulation performance and noise immunity are exhibited, huge calculation amount is daunting.
In the embodiment of the present invention, it is noted from the expressions (17) and (18) that the target equivalent speed VeNo blurring velocity v0And the true velocity v can be estimated simultaneously by means of the angle of rotation, i.e.
According to equation (12), the unambiguous velocity can be solved by
Wherein ROUND (-) is an integer function,
is the doppler ambiguity number of the target. So the true velocity of the target is estimated as
Using estimated
A new phase compensation function is constructed which,
the linear phase term can be compensated by multiplying equation (22) by equation (18). Then, energy accumulation and parameter estimation can be completed by utilizing efficient NuFFT:
where p (-) represents the impulse response function of NuFFT,
to correspond to (m' T)2Is measured.
As can be seen from equation (23), the target energy accumulates as a single peak, and from its position, the acceleration of the target can be estimated as:
further, in the embodiment of the invention, a phase compensation function is constructed according to the estimated target speed and acceleration; carrying out slow time Fourier transform and range frequency Fourier inverse transform on the maneuvering target signal model to complete coherent accumulation; obtaining a single spectral peak of a distance-Doppler domain after the mobile target receiving signal is accumulated; target detection is accomplished by a single spectral peak. Preferably, the target detection is finished by using a constant false alarm aiming at a single spectrum peak value, if the peak value exceeds a self-adaptive threshold, a moving target is judged to exist, and a moving target parameter is obtained; and if the peak value is smaller than the threshold, judging that the moving target is not detected.
And constructing a phase compensation function according to the estimated target speed and acceleration to eliminate the influence of LRM, QRM and DFM:
finally, the coherent accumulation can be completed by carrying out the slow time FT and the distance frequency IFT on the formula (7):
wherein A isCIIs a complex amplitude, fdTo correspond to a slow time tmThe doppler frequency of (d).
In equation (26), the received signal of the maneuvering target is accumulated as a single spectral peak in the range-doppler domain, whose peak value is | SCI(2R0/c,-2v0Lambda) |. Target detection can then be performed using Constant False Alarm (CFAR) techniques:
if the peak value exceeds the self-adaptive threshold eta, the moving object is indicated, and the moving parameter is
And
otherwise, if | SCI(2R0/c,-2v0And/lambda) is less than the threshold eta, no moving object is detected.
Suppose Q targets are observed within the coherent integration time. Similar to equation (14), the signal after passing through SoKT can be expressed as:
LRT of formula (28) gives:
when in use
Then the LRM of the ith target is corrected, while the LRMs of the remaining q-1 targets still exist, i.e.
Then, along the distance unit nR0,iExtracting the direction signal can obtain:
wherein,
similar to equations (19) to (22), the phase compensation function is constructed from the rotation angle
Multiplying equation (31) by equation (33) and performing NuFFT to focus the target energy and estimate acceleration yields:
note that only the ith target energy is concentrated to a single peak, while the other target energies are divergent, mainly due to two reasons: (a) the envelopes of the other q-1 objects are distributed among different range bins; (b) the linear phase term exp (-j4 π v) in equation (32)0,qm' T/λ) cannot be compensated by the phase compensation function H1(m') so that NuFFT cannot gather its energy.
Finally, coherent accumulation and parameter estimation of the ith target can be completed, however, the rest q-1 targets cannot realize focusing by using the parameters of the ith target.
LRT-NuFFT correction of LRM is realized through rotation angle search. However, the LRT process requires a large number of interpolation operations, which inevitably results in numerical errors, and in addition, the rotation angle is not a natural representation of the target motion parameters. Because, in order to solve this problem, the embodiment of the present invention further provides an interpolation-free implementation form of LRT-NuFFT, i.e. speed search-NuFFT (VS-NuFFT).
Considering the coordinate rotation transformation in equation (16), it can be rewritten as:
wherein,
is the rotation angle of the search.
FT along the fast time for equation (35) can be obtained:
as can be seen from equation (36), LRT operation can be achieved by phase compensation from the frequency domain, where the compensation function is
When V iss＝Ve，frAnd m' are eliminated. IFT is performed along the range frequency for equation (36) to obtain:
it is noted that the velocity search interval should be less than Δ v ═ λ/2N, depending on doppler frequency resolutionaT。
Further, based on the above method, an embodiment of the present invention further provides a coherent detection apparatus for a maneuvering target based on coordinate rotation and non-uniform fourier transform, as shown in fig. 2, including: a model building module 101 and a joint estimation module 102, wherein,
the model establishing module 101 is used for establishing a maneuvering target signal model aiming at radar signals;
and the joint estimation module 102 is used for performing range migration processing on the maneuvering target signal model and estimating target motion parameters through coordinate rotation and non-uniform Fourier transform.
In order to further verify the effectiveness of the invention, the following explains the technical scheme of the invention by combining a specific implementation algorithm and a simulation experiment:
referring to fig. 3, the specific steps of the LRT-NuFFT algorithm can be designed as follows:
step 1: the radar echo is subjected to pulse compression to obtain
Step 2: the distance direction FT. SoKT eliminates QRM. Distance IFT is given as sSoKT(n,m)；
And step 3: according to the detectionDetermining a rotation angle search range
Search interval
Should be less than arctan (lambda/2N)aρ)；
And 4, step 4: LRT is carried out on the formula (17) under a certain rotation angle, and then a rotated signal s is obtainedrot(n′,m′)；
And 5: constructing a phase compensation function H according to the rotation angle1(m') as shown in formulas (19) to (22). Extracting the slow time signal to obtain sazi(m′)；
Step 6: to sazi(m′)·H1(m') performing NuFFT to realize signal energy accumulation and estimate acceleration, as shown in equation (23);
and 7: when the initial distance and the search angle match with the target true value, the output of NuFFT reaches a maximum value. Estimating a target speed and acceleration from equations (21) to (24);
and 8: construction of a further phase compensation function H using the estimated motion parameters2(fr,tm) And realizing coherent accumulation as shown in formulas (25) and (26).
Compared with the existing MART-LVT and LRT-NuFFT algorithms, the method has the following improvements and advantages: (1) in MART-LVT, QRM effects due to target acceleration are ignored, which can cause cumulative performance loss in certain scenarios. Whereas in LRT-NuFFT the QRM is first eliminated with SoKT, which makes the algorithm more suitable for highly mobile object detection. (2) Unlike MART-LVT, LRT-NuFFT utilizes the rotation angle
And a no-blur velocity v0The relationship between them, the phase compensation is innovatively constructed. Therefore, the subsequent energy accumulation and acceleration estimation can be quickly achieved by NuFFT, which greatly eases the computational burden of computing the LVT algorithm. Compared with MART-LVT and KT-LVD. TDST, SAF-SFT and ACCF algorithm, LRT-NuFFT algorithm proposed in the embodiment of the present invention does not involve nonlinear operation, therefore, the algorithm does not have cross terms, and satisfies linear property; although LVD, TDST, SAF-SFT and ACCF have satisfactory cross term suppression capability, weak targets are still easily submerged in cross terms caused by strong targets. The rotation angle search is equivalent to the speed search, so that the interpolation process can be realized by utilizing FT, phase compensation and IFT, and the introduction of numerical errors is avoided; in addition, the speed search is more intuitive than the angle search, and the result is also the natural expression of the target motion parameter; in terms of search interval, a uniform search of angles corresponds to a non-uniform sampling of velocity, which is approximately equal at small angles but exhibits significant differences at large angles.
The LRT-NuFFT algorithm's calculated quantities were analyzed and compared to representative MART-LVT, KT-LVD, TDST, SAF-SFT, and MLRT algorithms as follows:
suppose that
NF、NrAnd NaThe angle search number, folding factor search number, distance cell number, and pulse number are respectively represented.
The main steps of the MART-LVT algorithm include the MART operation
And LVT operation
Therefore, the total calculation amount is
Magnitude.
For the KT-LVD algorithm, the main step involves a folding factor search (O (N)FNr) And LVD operation
Therefore, the total calculation amount is about
Magnitude.
For the TDST algorithm, the need arises
And
a two-step scale-variable fourier transform (SFT) is calculated. Therefore, the total calculation amount is about
Magnitude.
The main steps of the SAF-SFT algorithm include two SFT operations with a computational complexity of O (3N) respectivelyaNrlog2Na) And
therefore, the total calculation amount is O (3N)a(Na+Nr)log2Na) Magnitude.
The amount of MLRT calculation can be easily derived
Magnitude.
Unlike the MART-LVT algorithm, the algorithm proposed in the embodiment of the present invention uses phase compensation and NuFFT to reduce the computational complexity, so the total computation amount is also about
The calculated amount of the above algorithm is given in table 1. Suppose that
NKTFig. 4 shows a graphical representation of the calculated amount of the algorithm, 10. It is clear that the LRT-NuFFT algorithm has a lower computational load than MART-LVT, KT-LVD and TDST.
TABLE 1 different algorithm computational complexity
The algorithm of the invention is analyzed in combination with radar simulation data, and the simulation radar parameters are shown in table 2:
TABLE 2 simulated Radar parameters
First, the coherent accumulation capability of the proposed algorithm is evaluated with a single target. The target motion parameters are: r0＝150km，v＝150m/s，a＝8m/s2. Figure 5(a) shows the pulse compression result, where the signal-to-noise ratio is-10 dB. Obviously, the target trajectory is overwhelmed by strong noise and cannot be observed. In addition, fig. 5(b) shows the accumulation result of MLRT. MLRT cannot detect maneuvering targets because QRM and DFM are not considered. Fig. 5(c) and 5(d) show the accumulation results of the SAF-SFT and TDST algorithms, respectively. Because the non-linear transformation loses some of the signal energy, the output results of both algorithms remain buried in noise. In contrast, MART-LVT and LRT-NuFFT are able to accumulate targets as a single peak at corresponding locations in the range-Doppler domain, as shown in FIGS. 5(e) and 5 (f). The simulation experiment shows the coherent accumulation capacity of the proposed algorithm in a low signal-to-noise ratio environment.
Secondly, the accumulation performance under multiple targets is analyzed. The motion parameter settings for the two motorized targets (Tr1 and Tr2) are given in table 3. Fig. 6(a) shows the pulse compression results. Fig. 6(b) shows the velocity search results, where two distinct spectral peaks give the equivalent velocities of Tr1 and Tr 2. From this, the true speeds of Tr1 and Tr2 are estimated as:
the LRMs of the two targets can be corrected separately using this parameter, as shown in fig. 6(c) and 6 (d). The accelerations through NuFFT, Tr1 and Tr2 can be estimated in the corresponding distance units, resulting in
As shown in fig. 6(e) and 6 (f). Finally, FIG. 6(g) and FIG. 6(h) show the coherent integration results for the two targets, respectively. The simulation experiment verifies the accumulation capacity of the algorithm to multiple targets.
TABLE 3 two maneuvering target simulation parameters
It is noted that many spurious peaks can be observed in fig. 6(b), which are spaced at a half-blind speed. Therefore, these spurious peaks are referred to as semi-blind velocity side lobes (HBSSLs). In practical situations, a weak target may be submerged in the HBSSLs of a strong target and may be processed using the CLEAN algorithm.
The target detection performance of the proposed algorithm was evaluated by Monte Carlo experiments, where the signal-to-noise ratio after pulse compression varied from-25 dB to 5 dB. At each signal-to-noise ratio, 500 independent Monte Carlo experiments were performed. False alarm rate is set to Pfa＝10-6. Five representative algorithms (MART-LVT, KT-LVD, TDST, SAF-SFT, and MLRT) were used as comparisons. The target detection probability curve is shown in fig. 7. Obviously, the LRT-NuFFT algorithm can obtain nearly ideal detection performance. This is because the LRT-NuFFT effectively eliminates the effects of LRM, QRM and DFM effects. MART-LVT and KT-LVD suffer some performance loss due to ignoring QRM caused by target acceleration. The detection thresholds of TDST and SAF-SFT algorithms are respectively 3dB and 6dB higher than the ideal case, which indicates that the bilinear transformation can lose a large amount of signal energy and deteriorate the anti-noise performance. MLRT does not take into account QRM and DFM effects and thus the detection performance is the worst.
The algorithm in the embodiment of the invention is further explained below by combining a commercial unmanned aerial vehicle 3 Xinjiang spirit, and data is collected in a certain campus. Fig. 8(a) and 8(b) show an experimental scenario and the Frequency Modulated Continuous Wave (FMCW) radar system used. The radar parameters are listed in table 4. To obtain doppler ambiguity of the target, the PRF of the radar is artificially reduced.
TABLE 4 FMCW Radar System parameters
Fig. 8(c) shows the target motion trajectory after pulse compression. Within the coherent accumulation time of 0.92s, the drone moves more than 7 range cells, causing severe range migration phenomena. Fig. 8(d) shows the equivalent velocity estimation result. The speed search range is set to [ -10,10 [)]m/s, the search interval is 0.01 m/s. From the peak position, the equivalent velocity of the UAV can be derived as
True velocity of
The LRM is effectively corrected by phase compensation, and the acceleration of the UAV is obtained through NuFFT, namely
As shown in fig. 8(e) and 8(f), respectively. Fig. 8(g) shows the accumulation of LRT-NuFFT, and it can be seen that a well-focused spectral peak is formed in the range-doppler domain. Meanwhile, the accumulation results of the MTD and MLRT are given in fig. 8(h) and 8(i) for reference. However, it can be seen that the energy of the target is spread among multiple range and doppler cells, making target detection difficult; further verifies that the LRT-NuFFT algorithm can obtain nearly ideal detection performance with lower calculation amount in the embodiment of the invention, and verifies the effectiveness of the technical scheme in the embodiment of the invention through UAV experimental results.
Unless specifically stated otherwise, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.
Based on the foregoing method, an embodiment of the present invention further provides a server, including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method described above.
Based on the above method, the embodiment of the present invention further provides a computer readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the above method.
The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A maneuvering target coherent detection method based on coordinate rotation and non-uniform Fourier transform is characterized in that,
A) aiming at radar signals, establishing a maneuvering target signal model;
B) carrying out range migration processing on the maneuvering target signal model, and jointly estimating target motion parameters through coordinate rotation and non-uniform Fourier transform;
A) the method for establishing the maneuvering target signal model comprises the following steps: assuming that the radar transmits a linear frequency modulation signal, the maneuvering target moves at constant acceleration, and the instantaneous slope distance of the target from the radar is obtained according to the slow time variable, the initial slope distance of the target, the radial speed and the acceleration; obtaining radar receiving signals according to the instantaneous slope distance, the target radial speed, the signal amplitude and the bandwidth;
fourier transformation is carried out on the radar receiving signals along a fast time axis to obtain a maneuvering target signal model, and the maneuvering target signal model is expressed as follows:
wherein f isrIs relative to
Doppler frequency of fcIs the carrier frequency of the signal and,
representing a fast time variable, tmDenotes a slow time variable, AfAnd B is the signal amplitude and bandwidth, v, respectivelyb＝λfpA/2 and fpRespectively representing radar blind speed and pulse repetition frequency, NbIs a target Doppler ambiguity number, v0For no blurring speed, R0And a is the initial slope and acceleration of the target, respectively, and λ ═ c/fcIs the signal wavelength;
B) the intermediate range migration treatment comprises elimination treatment of linear range migration, secondary range migration and Doppler frequency migration;
B) in the method, second-order Keystone transformation is adopted to eliminate secondary range migration caused by maneuvering target acceleration; the second-order Keystone transformation eliminates the secondary range migration process, and comprises the following contents: scaling the slow time of each distance frequency unit, and bringing the scaled slow time variable into a maneuvering target signal model; simplifying and expressing a maneuvering target signal model obtained after scaling through Taylor expansion, performing Fourier inverse transformation along range frequency, completing secondary coupling between Doppler frequency and slow time variable, and correcting a secondary range migration effect caused by a maneuvering target;
B) in the method, the jointly estimated target motion parameters include the following: representing the maneuvering target signal model as a discrete form under a coordinate system; eliminating residual linear distance migration effect by introducing coordinate rotation transformation, extracting signal energy along the azimuth direction, and estimating the equivalent speed, the unambiguous speed and the real speed of the maneuvering target by using the rotation angle; constructing a phase compensation function according to the estimated non-fuzzy speed, and multiplying the phase compensation function by the extracted signal energy to compensate a linear phase term; carrying out parameter estimation by utilizing non-uniform Fourier transform;
in the parameter estimation by utilizing the non-uniform Fourier transform, firstly, the non-uniform Fourier transform is carried out on the multiplication result, the situation that the energy of the maneuvering target is accumulated into a single peak value is judged according to the non-uniform Fourier transform, and the acceleration of the maneuvering target is estimated from the single peak value situation, wherein the non-uniform Fourier transform is expressed as:
the maneuvering target acceleration estimate is expressed as:
p (-) represents the impulse response function of the non-uniform fourier transform,
to correspond to (m' T)2Is the rotated coordinate, nR0Is R0T is the pulse repetition time and K is the over-sampling rate.
2. The method for coherent detection of maneuvering targets based on coordinate rotation and non-uniform Fourier transform according to claim 1, characterized in that a phase compensation function is constructed according to the estimated target speed and acceleration; carrying out slow time Fourier transform and range frequency Fourier inverse transform on the maneuvering target signal model to complete coherent accumulation; obtaining a single spectral peak of a distance-Doppler domain after the mobile target receiving signal is accumulated; target detection is accomplished by a single spectral peak.
3. The method for detecting the coherent moving target based on the coordinate rotation and the non-uniform Fourier transform as claimed in claim 1 or 2, wherein the target detection is completed by using a constant false alarm for a single spectral peak value, and if the peak value exceeds a self-adaptive threshold, a moving target is determined to exist, and a moving target parameter is obtained; and if the peak value is smaller than the threshold, judging that the moving target is not detected.
4. A mobile object coherent detection device based on coordinate rotation and non-uniform Fourier transform, which is realized based on the method of claim 1 and comprises the following steps: a model building module and a joint estimation module, wherein,
the model establishing module is used for establishing a maneuvering target signal model aiming at the radar signal;
and the joint estimation module is used for carrying out range migration processing on the maneuvering target signal model and estimating target motion parameters through coordinate rotation and non-uniform Fourier transform.
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