CN104656062A - Method for restraining dual-mode adaptive direct wave and noise wave of passive bistatic system - Google Patents
Method for restraining dual-mode adaptive direct wave and noise wave of passive bistatic system Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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Abstract
The invention discloses a method for restraining dual-mode adaptive direct wave and noise wave of a passive bistatic system. The method comprises the following steps: firstly, calculating the weight coefficient vector of an adaptive filter through a self-correlation algorithm; secondly, performing iterative convergence through an adaptive least mean square error (LMS) algorithm. The method adopts a dual mode compensation algorithm based on the integration of a cross-correlation algorithm and an adaptive LMS algorithm during the compensation of the direct wave and the noise wave or reverberation, and the dual-mode compensation algorithm has the characteristics of high rapidness in convergence and small calculated amount at the same time.
Description
Technical Field
The invention relates to a self-adaptive direct wave and clutter suppression method, in particular to a dual-mode self-adaptive direct wave and clutter suppression method of a passive bistatic system.
Background
The conventional active radar or sonar seriously interfere with each other when working at the same time, and the position is easily exposed. And the receiving base can effectively avoid the reconnaissance and attack of enemies because the two base systems do not transmit and receive at one place. Some bistatic systems use a self-cooperating illumination source to achieve target detection, while other bistatic systems use other non-cooperating transmission signal sources. Such bistatic systems employing non-cooperative illumination sources are commonly referred to as passive dual multistatic systems. The radar or sonar system with the system is flexible to use and strong in concealment, so that the radar or sonar system has a good application prospect.
Passive bistatic systems using non-cooperative illumination sources have been widely studied and applied. Unintentional illumination source signal forms such as fm broadcasts, GSM handset signals, WiFi signals, high frequency radar signals, etc., may be used as non-cooperative illumination sources for passive bistatic radars. The passive bistatic sonar system adopts non-cooperative continuous wave signals such as underwater interference, communication signals and the like to complete bistatic and multistatic detection. For passive bistatic systems, a key technique is cancellation processing of direct waves and multipath signals. There are a lot of literature researches on direct wave suppression of a bistatic radar and sonar system, and a filtering method such as adaptive Least Mean Square (LMS) is generally adopted for suppression. The main problem with this algorithm is that the presented method is difficult to reconcile in terms of both computation speed and convergence speed. The algorithm with higher convergence rate, such as the sampling matrix inverse algorithm, provided by the existing literature has too large calculation amount and cannot be realized in an actual system; although an algorithm with a small calculation amount, such as an LMS algorithm, can be realized in real time, the convergence speed of the algorithm is too slow, so that residual clutter (sonar is reverberation) is large, and the detection of signals is influenced.
Disclosure of Invention
In order to solve the technical problem, the invention provides a dual-mode adaptive direct wave and clutter suppression method of a passive bistatic system, which has small convergence and fast calculation amount.
In order to achieve the purpose, the invention adopts the technical scheme that:
the dual-mode adaptive direct wave and clutter suppression method of the passive bistatic system comprises the following steps,
step one, calculating a weight coefficient vector W of an adaptive filter by adopting an autocorrelation algorithm;
W＝v*/r
wherein,
r is the autocorrelation with a time delay of 0; <math>
<mrow>
<mi>r</mi>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<msub>
<mi>N</mi>
<mi>f</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msup>
<mi>y</mi>
<mo>*</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
</math> Nffor the sample point length of the time segment taken, y (k) for the beam output at time k obtained using digital beamforming techniques for the monitoring channel, [ ·]*Represents a conjugate operation;
v is the cross-correlation with a delay of 0;x1(k) a signal received for a reference channel at time k;
step two, iterative convergence is carried out by adopting an LMS algorithm, and the iteration is carried out by NxSampling points in time;
iterate the formula of
Wherein,
w (k) is N obtained in the step one at the time point kfA x 1-dimensional weight coefficient vector, W (k +1) is k +1Obtained NfX 1-dimensional weight coefficient vector;
μ0is a step size factor;
X1(k) a recursive vector is input for the adaptive filter,
X1(k)＝[x1(k)x2(k-1)…x1(k-i)…x1(k-N+1)]T
u (k) is the output error,
u(k)＝y(k-f)-X1 H(n)W(k)
[·]Hwhich represents the conjugate transpose operation, is,fis a time delay amount, is an integer sampling interval and takes a value of 0 or lessf＜Nf，y(k-f) Obtaining k-fThe beam output at the time.
The invention achieves the following beneficial effects: in the process of canceling direct waves and clutter or reverberation, the invention adopts a dual-mode cancellation algorithm based on the combination of cross-correlation operation and a self-adaptive minimum mean square error algorithm, and the dual-mode cancellation algorithm has the characteristics of quick convergence and small calculated amount.
Drawings
Fig. 1 is a schematic block diagram of passive bistatic system target location.
Fig. 2 is a block diagram of a passive bistatic system process flow.
Fig. 3 is a schematic block diagram of the present invention.
FIG. 4 shows the output signal of the convergence process of the LMS algorithm.
Fig. 5 shows the signal condition of the LMS algorithm from the doppler plane.
Fig. 6 is an output signal obtained by the dual mode cancellation algorithm.
Fig. 7 shows the signal situation in the range-doppler plane for the dual-mode cancellation algorithm.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, for a passive bistatic system, when a non-cooperative radiation source (transmitting base) radiates an operating waveform, a direct wave of a transmitted signal reaches a receiving base, and a plurality of reflection multipaths are received. The target echo at point T is also received by the receiving base at point R. The positions of the transmitting base and the receiving base are known mutually, one side RS of the triangle RST is known, the included angle between the RT side and the RS side can be measured, and the bistatic time difference of the RT + TS-RS side can be measured, so that an equation set can be formed, and the position of the target can be solved.
As shown in fig. 2, a passive bistatic system process flow diagram. A passive bistatic system has two channels: 1) a reference channel for mainly receiving the direct wave; 2) the monitoring channel is mainly used for Digital Beam Forming (DBF) and adaptive filtering to suppress direct waves and multipath. And the direct wave signal of the reference channel and the signal of the monitoring channel are adopted to complete the distance Doppler cross-correlation calculation and the target detection.
Assuming that the transmitted signal at the time k is d (k), the reference channel at the time k of the passive bistatic system can receive a signal x by taking the noise of the reference channel receiver into consideration1(k) Written in the form of a write beam that,
x1(k)＝d(k)+n1(k)
n1(k) the receiver noise of the reference channel is taken as the k time.
Suppose that the monitoring channel is composed of an N unit equidistant linear array, and the ith unit of the monitoring channel at the moment k is connected withReceive signal x2i(k) In order to realize the purpose,
in the formula n2(k) Monitoring channel receiving end noise for k time, h ═ h0,h1,…,hL-1]TRepresenting a channel response reaching the monitoring channel, L being the length of the channel response;
xT(t) is a target signal, having the form,
in the formula taumBistatic time delay, f, for the mth targetdmBistatic Doppler frequency, F, for the m-th targetsM is the target number for the sampling rate.
Using a Digital Beamforming (DBF) technique for the monitor channel, the beam output at time k is obtained as follows:
where λ represents the wavelength of the acoustic wave signal, θ represents the arrival angle of the signal, and d0Showing the spacing of two units of an equidistant bar.
Combining the above processing flow, filtering the passive bistatic direct wave channel and monitoring channel signals by using a least mean square adaptive filtering algorithm (LMS algorithm), filtering the direct wave signals and reflected multipath signals, introducing a cross-correlation complex weight calculation for accelerating the convergence rate of the algorithm, and initializing the LMS algorithm to form the dual-mode adaptive direct wave and clutter suppression method of the passive bistatic system shown in fig. 3, which specifically comprises the following steps:
step one, calculating a weight coefficient vector W of an adaptive filter by adopting an autocorrelation algorithm;
W＝v*/r
wherein,
r is the autocorrelation with a time delay of 0;Nflength of sampling point of the time segment taken [. ]]*Represents a conjugate operation;
v is the cross-correlation with a delay of 0; <math>
<mrow>
<mi>v</mi>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<msub>
<mi>N</mi>
<mi>f</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<msub>
<mi>x</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msup>
<mi>y</mi>
<mo>*</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
</math>
step two, iterative convergence is carried out by adopting an LMS algorithm, and the iteration is carried out by NxSampling points in time;
iterate the formula of
Wherein,
w (k) is N obtained in the step one at the time point kfX 1-dimensional weight coefficient vector, W (k +1) being N obtained in step one at time k +1fX 1-dimensional weight coefficient vector;
μ0is a step size factor;
X1(k) a recursive vector is input for the adaptive filter,
X1(k)＝[x1(k)x2(k-1)……x1(k-N+1)]T
u (k) is the output error,
u(k)＝y(k-f)-X1 H(n)W(k)
[·]Hwhich represents the conjugate transpose operation, is,fis a time delay amount, is an integer sampling interval and takes a value of 0 or lessf＜Nf，y(k-f) Obtaining k-fThe beam output at the time.
Taking a passive bistatic system based on frequency modulation broadcast signals as an example, the performance of the invention is verified.
The transmitting station transmits frequency modulation signals, the system bandwidth is set to be 50kHz, the IQ orthogonal data rate is set to be 100kHz, the accumulation time is set to be 1s, the number of DBF receiving channels is set to be 7, a formed beam points to 23 degrees, 2 targets exist in the beam, bistatic time delay and Doppler gate are respectively (150km, -105Hz), (249km, 64Hz), the incident angle of direct wave signal is-50 degrees, the vector length of weight coefficient of adaptive cancellation filter is 32, the data length extracted by cross correlation algorithm is 1000 points, for the weight coefficient vector calculation, the step size factor of the LMS algorithm is selected to be 0.001, the ratio of the target signal to the direct wave and the multipath reflection signal is-50 dB, the ratio of the direct wave to the noise is 0dB (before matched filtering), the direct wave antenna is aligned to the direction of the transmitting station to complete the direct wave extraction, and a monitoring channel from the transmitting base to the receiving base has the following multipath propagation paths:
h＝[1,-0.4ej0.7,0.15e-j1.3]T
comparing fig. 4 and fig. 6, it can be seen that, in the present invention, since the cross-correlation algorithm is used as the initialization calculation of the weight coefficient vector, from the beginning of iteration, the output signal modulus is in a smaller stable convergence state, which shows a smaller convergence error, and is smaller than that of the LMS algorithm. As can be seen from fig. 5 and 7, compared with the range-doppler plane obtained by the LMS algorithm, the range-doppler detection plane obtained by the present invention is lower, by about 12dB, and can complete detection on 2 targets, whereas the original LMS algorithm cannot complete signal detection due to high noise floor.
In conclusion, in the cancellation process of direct waves and clutter or reverberation, the dual-mode cancellation algorithm based on the combination of cross-correlation operation and the adaptive minimum mean square error algorithm is adopted, and the dual-mode cancellation algorithm has the characteristics of quick convergence and small calculated amount and is suitable for a passive bistatic system.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (1)
1. The dual-mode self-adaptive direct wave and clutter suppression method of the passive bistatic system is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
step one, calculating a weight coefficient vector W of an adaptive filter by adopting an autocorrelation algorithm;
W＝v*/r
wherein,
r is the autocorrelation with a time delay of 0;Nffor the sample point length of the time segment taken, y (k) for the beam output at time k obtained using digital beamforming techniques for the monitoring channel, [ ·]*Represents a conjugate operation;
v is the cross-correlation with a delay of 0;x1(k) a signal received for a reference channel at time k;
step two, iterative convergence is carried out by adopting an LMS algorithm, and the iteration is carried out by NxSampling points in time;
iterate the formula of
Wherein,
w (k) is N obtained in the step one at the time point kfX 1-dimensional weight coefficient vector, W (k +1) being N obtained in step one at time k +1fX 1-dimensional weight coefficient vector;
μ0is a step size factor;
X1(k) a recursive vector is input for the adaptive filter,
X1(k)＝[x1(k)x2(k-1)…x1(k-i)…x1(k-N+1)]Tu (k) is the output error,
[·]Hwhich represents the conjugate transpose operation, is,fis a time delay amount, is an integer sampling interval and takes a value of 0 or lessf＜Nf，y(k-f) Obtaining k-fThe beam output at the time.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107886050A (en) * | 2017-10-16 | 2018-04-06 | 电子科技大学 | Utilize time-frequency characteristics and the Underwater targets recognition of random forest |
| CN109143231A (en) * | 2018-10-29 | 2019-01-04 | 河海大学 | The DTV passive bistatic radar object detection method offseted based on circulation |
| CN111007486A (en) * | 2019-12-13 | 2020-04-14 | 中国人民解放军海军工程大学 | Active sonar reverberation suppression method based on multi-orthogonal signals |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107886050A (en) * | 2017-10-16 | 2018-04-06 | 电子科技大学 | Utilize time-frequency characteristics and the Underwater targets recognition of random forest |
| CN109143231A (en) * | 2018-10-29 | 2019-01-04 | 河海大学 | The DTV passive bistatic radar object detection method offseted based on circulation |
| CN111007486A (en) * | 2019-12-13 | 2020-04-14 | 中国人民解放军海军工程大学 | Active sonar reverberation suppression method based on multi-orthogonal signals |
| CN111007486B (en) * | 2019-12-13 | 2022-01-04 | 中国人民解放军海军工程大学 | Active sonar reverberation suppression method based on multi-orthogonal signals |
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