CN113050053B - Method and system for acquiring coherent parameters of distributed coherent radar on moving platform - Google Patents

Abstract

The application relates to a method and a system for acquiring phase parameters of a dynamic platform distributed phase-coherent radar. The method comprises the following steps: modeling a dynamic platform distributed coherent radar detection scene to obtain a dynamic platform emission signal model, a dynamic platform emission coherent model and a dynamic platform receiving coherent model, and then establishing a dynamic platform coherent calculation model according to the dynamic platform emission coherent model and the dynamic platform receiving coherent model; according to the coupling relation between the emission phase parameters, a state vector of the emission phase parameters is established, and a Singer model is adopted to model the state vector, so that a state equation of the emission phase parameters is obtained; according to the state equation, an observation equation is determined, and according to the observation equation, a Kalman filtering equation is determined, so that the coherent radar coherent parameters are calculated. The method can obtain the coherent radar coherent parameters of the mobile platform.

Description

Method and system for acquiring coherent parameters of distributed coherent radar on moving platform

Technical Field

The present application relates to the field of signal processing technology, and in particular to a method and system for acquiring coherent parameters of a distributed coherent radar on a moving platform.

Background Art

The widespread use of low-speed, slow, and small drones has brought convenience to various fields of society, but it also seriously threatens the safety of low-altitude areas. In recent years, accidents caused by drones have increased day by day, and it is urgent to study effective detection technology for drone targets in low-altitude areas. Due to the small reflection area of low-speed, slow, and small drone targets, the received echo of a single radar often has insufficient signal-to-noise ratio, making detection difficult. The core idea of distributed coherent radar is to make multiple transmission signals superimposed at the target at the same time, so that the echo signal-to-noise ratio is greater than that of a single radar, thereby improving the detection capability of small targets. Distributed coherent radar makes the signals between radars coherent by estimating the transmission and reception delay difference and phase difference between each unit radar. This parameter is called coherent parameters (CPs). However, due to the maneuverability of the target drone, if a distributed coherent radar with a fixed platform is adopted, the target can easily fly out of the radar's field of view. The distributed coherent radar based on the moving platform has incomparable advantages over the fixed platform distributed coherent radar. Taking the UAV platform as an example, it has strong maneuverability, small terrain restrictions, and improved detection capabilities. However, the mobility of the platform will bring many challenges. The lag of coherent parameter estimation is an inevitable problem for the practical application of the distributed coherent radar of the moving platform. Because when the target and the platform are in relative motion, the coherent parameters recognized at the previous moment cannot be directly used for the coherent emission at the current moment, and the corresponding prediction method needs to be studied.

Summary of the invention

Based on this, it is necessary to provide a method and system for acquiring coherent parameters of a moving platform distributed coherent radar that cannot be achieved by a moving platform distributed coherent radar in response to the above-mentioned technical problems.

A method for acquiring coherent parameters of a distributed coherent radar on a moving platform, the method comprising:

The moving platform distributed coherent radar detection scene is modeled to obtain a moving platform transmission signal model, a moving platform transmission coherent model, and a moving platform reception coherent model; the moving platform distributed coherent radar detection scene is composed of multiple independent moving platforms, and the moving platforms are independent of each other; the moving platforms transmit data through wireless links;

Establishing a motion platform coherence calculation model according to the motion platform transmitting coherence model and the motion platform receiving coherence model;

According to the coupling relationship between the emission coherent parameters, a state vector of the emission coherent parameters is established, and the state vector is modeled by using the Singer model to obtain a state equation of the emission coherent parameters;

Determine an observation equation according to the state equation, and determine a Kalman filter equation according to the observation equation;

According to the Kalman filter equation, the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter is determined, and according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained.

In one embodiment, the method further includes: modeling a moving platform distributed coherent radar detection scene, and obtaining a moving platform transmission signal model of the m-th moving platform transmission signal as follows:

Where, _Tp is the transmit pulse width, u is the frequency modulation slope, rect(t) is the rectangular function, is the carrier, s _m (t) = exp(j2π(m-1)Δft);

m represents the number of the moving platform when transmitting the signal, and l represents the number of the moving platform when receiving the signal. Then the signal transmitted by the mth moving platform is:

Where κ _m represents the synchronization error of radar m compared to the reference clock, is the initial phase of radar m.

In one embodiment, the signal emitted by the m-th moving platform and reaching the target is expressed as:

Where τ _m represents the arrival delay of the signal emitted by the mth moving platform, κ _m is the synchronization error of the radar relative to the reference clock, is the synchronization error of the radar relative to the reference phase;

The total signal reaching the target is:

Set radar 1 as the reference radar, then the adjusted transmission signals can be expressed as:

in and is the launch coherent parameter, and the launch coherent model of the moving platform is obtained as follows:

In one embodiment, the method further includes: if the lth moving platform receives the target reflected echo, it is expressed as:

Where p(t) is the echo signal at the target;

Then the target echoes received by all radars are superimposed as:

Set radar 1 as the reference radar, and the adjusted received signals are expressed as:

in, and To receive the coherent parameters, the moving platform receiving coherent model is obtained as:

In one embodiment, the method further includes: establishing a moving platform coherent calculation model according to the moving platform transmitting coherent model and the moving platform receiving coherent model:

in,

Where r _l (n) is directly measured by radar.

In one embodiment, the method further includes: establishing a state vector of the transmission coherent parameters according to the coupling relationship between the transmission coherent parameters:

Where R[n] represents the state vector, r _l (n) represents the transmission coherent parameter;

The state vector is modeled using the Singer model:

where α is the inverse of the maneuver-related time constant, i.e., the maneuver frequency, is the acceleration variance of the maneuvering target;

The state equation is established as:

R[n+1]＝Φ(T,α)R[n]+u[n]

The driving noise covariance is:

In one embodiment, the method further includes: determining an observation equation according to the state equation as:

z[n]＝HX[n]+v[n]

Where, H = [1 0 0], v[n] = σ ² , σ ² is the radar measurement noise;

According to the observation equation, the Kalman filter equation is determined as:

P[n|n-1]＝ΦP[n|n]Φ ^T +Q[n]

K[n]＝P[n|n-1]H ^T (HP[n|n-1]H ^T +R) ^-1

P[n|n]＝(I-K[n]H)P[n|n-1]

In one of the embodiments, the method further includes: determining, according to the Kalman filter equation, a predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter as:

According to the predicted emission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained as follows:

in,

A coherent parameter acquisition system for a distributed coherent radar on a moving platform, the system comprising:

A scene modeling module is used to model the moving platform distributed coherent radar detection scene to obtain a moving platform transmission signal model, a moving platform transmission coherent model, and a moving platform reception coherent model; the moving platform distributed coherent radar detection scene is composed of a plurality of independent moving platforms, and the moving platforms are independent of each other; the moving platforms transmit data through wireless links;

A Kalman filter module is used to establish a motion platform coherent calculation model according to the motion platform transmission coherent model and the motion platform receiving coherent model; establish a state vector of the transmission coherent parameters according to the coupling relationship between the transmission coherent parameters, use the Singer model to model the state vector, and obtain the state equation of the transmission coherent parameters; determine the observation equation according to the state equation, and determine the Kalman filter equation according to the observation equation;

The coherent parameter calculation module is used to determine the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter according to the Kalman filter equation, and obtain the coherent radar coherent parameters according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model.

The above-mentioned moving platform distributed coherent radar coherent parameter acquisition method and system utilize the coupling relationship between the transmission coherent parameters and the distances between different platforms and targets, measure the distance between the target and the platform and obtain the distance change sequence, design a Kalman filter to make a one-step prediction of the distance change sequence, and estimate the transmission coherent parameters at the next moment according to the predicted distance value, which provides research support for the coherent parameter acquisition and system design of the moving platform distributed coherent radar.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a process of acquiring coherent parameters of a distributed coherent radar on a moving platform in one embodiment;

FIG2 is a schematic diagram of a moving platform detecting a target in one embodiment;

FIG3 is a schematic diagram of a coherent parameter estimation process in one embodiment;

FIG4 is a diagram of a simulation scene of a distributed coherent radar on a moving platform in one embodiment;

FIG5 is a diagram of the distance error measured by each radar in one embodiment (based on the Singer model);

FIG. 6 is a diagram showing the distance error of each radar measurement in one embodiment (without the Singer model)

FIG. 7 is a diagram showing a variation of a coherent parameter error of a radar 2 in one embodiment;

FIG8 is a diagram showing a variation of a coherent parameter error of radar 3 in one embodiment;

FIG9 is a structural block diagram of a moving platform distributed coherent radar coherent parameter acquisition system in one embodiment.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

In one embodiment, as shown in FIG1 , a method for acquiring coherent parameters of a distributed coherent radar on a moving platform is provided, comprising the following steps:

Step 102, modeling the moving platform distributed coherent radar detection scene to obtain a moving platform transmission signal model, a moving platform transmission coherent model, and a moving platform reception coherent model.

The mobile platform distributed coherent radar can be composed of several mobile platforms. Each mobile platform is independent of each other and can transmit data through a wireless link. Therefore, there are multiple working modes to choose from. In the MIMO mode, each mobile platform transmits an orthogonal signal and receives the signal at the same time. Because the transmitted signal is an orthogonal signal, the echoes transmitted by different mobile platforms can be separated by filtering and other methods at the receiving mobile platform end, and then the signal of a certain channel is selected as the reference signal. The signals of the remaining channels are based on the reference channel signal and aligned with the reference signal in terms of delay and phase. At this time, the receiving coherence is completed. After obtaining accurate coherence parameters through the MIMO mode, the system can switch to the full coherence working mode. In the full coherence mode, the system controls the time and initial phase of the signals transmitted by different mobile platforms, so that the signals transmitted by all mobile platforms arrive at the target at the same time and with the same phase, thereby forming a superposition at the target to achieve transmission coherence. On this basis, the superimposed echoes are processed. The following discusses the two working modes of the mobile platform distributed coherence system. When the system works in MIMO mode, the echoes of signals transmitted by different moving platforms need to be separated, so an orthogonal signal is required. Here, an orthogonal frequency division signal is used to transmit the waveform.

Step 104: Establish a motion platform coherent calculation model based on the motion platform transmitting coherent model and the motion platform receiving coherent model.

Because the distance movement caused by the relative motion of the moving platform and the target cannot be ignored, the coherence parameters will also change accordingly. This change is mainly caused by the change of the path difference. The purpose of transmission coherence is to cause forward interference at the target. However, due to the real-time change of the relative position of the platform and the target, and the coherence parameter cognition always occurs before the coherence synthesis, this means that the transmission coherence parameters recognized under only one observation cannot correct the transmission decoherence, that is, the recognized knowledge has a lag (time and phase synchronization errors are considered unchanged). In simple terms, what can be directly measured is. The detection scene is shown in Figure 2. In the platform (target) motion scene, it is necessary to make a reasonable prediction of the transmission coherence parameters to compensate for the decoherence caused by the lag of cognitive knowledge. In simple terms, the transmission coherence parameters at time n need to be determined based on the state at time n, but the latest state measured is still at time n-1.

Step 106: Establish a state vector of the transmission coherent parameters according to the coupling relationship between the transmission coherent parameters, use the Singer model to model the state vector, and obtain the state equation of the transmission coherent parameters.

Step 108, determining the observation equation based on the state equation, and determining the Kalman filter equation based on the observation equation.

Step 110, according to the Kalman filter equation, determine the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter, and obtain the coherent radar coherent parameters according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model.

In the above moving platform distributed coherent radar coherent parameter acquisition method, the coupling relationship between the transmission coherent parameters and the distances between different platforms and targets is utilized. By measuring the distance between the target and the platform and obtaining the distance change sequence, a Kalman filter is designed to make a one-step prediction of the distance change sequence, and the transmission coherent parameters at the next moment are estimated according to the predicted distance value, which provides research support for the coherent parameter acquisition and system design of the moving platform distributed coherent radar.

In one embodiment, a moving platform distributed coherent radar detection scenario is modeled, and the moving platform transmission signal model of the m-th moving platform transmission signal is obtained as follows:

Where, _Tp is the transmit pulse width, u is the frequency modulation slope, rect(t) is the rectangular function, is the carrier, s _m (t) = exp(j2π(m-1)Δft);

m represents the number of the moving platform when transmitting the signal, and l represents the number of the moving platform when receiving the signal. Then the signal transmitted by the mth moving platform is:

Where κ _m represents the synchronization error of radar m compared to the reference clock, is the initial phase of radar m.

In one embodiment, the signal emitted by the m-th moving platform and reaching the target is expressed as:

Where τ _m represents the arrival delay of the signal emitted by the mth moving platform, κ _m is the synchronization error of the radar relative to the reference clock, is the synchronization error of the radar relative to the reference phase;

The total signal reaching the target is:

Set radar 1 as the reference radar, then the adjusted transmission signals can be expressed as:

in and is the launch coherent parameter, and the launch coherent model of the moving platform is obtained as follows:

In one embodiment, if the lth moving platform receives the target reflected echo, it is expressed as:

Where p(t) is the echo signal at the target;

Then the target echoes received by all radars are superimposed as:

Set radar 1 as the reference radar, and the adjusted received signals are expressed as:

in, and To receive the coherent parameters, the moving platform receiving coherent model is obtained as:

Obviously, at the target, the signals received by each platform cannot be superimposed in the same direction. In order to achieve the coherent superposition of electromagnetic wave energy, that is, receiving coherence, it is necessary to adjust the receiving delay and receiving phase of each radar.

In one embodiment, it is assumed that the target and the platform can be approximately considered to be stationary within a single pulse. According to the moving platform transmission coherence model and the moving platform reception coherence model, the coherence parameters cannot be directly measured. In order to estimate the coherence parameters, each radar needs to transmit orthogonal signals first, and the receiving end separates the self-transmitted and self-received echoes and the self-transmitted and self-received echoes through matched filtering. The separated echoes are recorded as

Where τ _lm = τ _l + τ _m + κ _m - κ _l represents the time delay of the signal emitted by the mth moving platform reaching the lth moving platform after being reflected by the target, α _lm is the response of the scatterer on the path, is the phase synchronization error between the two radars.

The schematic diagram of the coherent parameter estimation process is shown in Figure 3. Through the definition of the coherent parameters, we can get:

When the system transmits the same waveform, the receiving end can still obtain the receiving coherent parameters, but cannot obtain the transmitting coherent parameters again. Therefore, the conversion relationship between the transmitting coherent parameters and the receiving coherent parameters must be established. By comparing the moving platform transmitting coherent model and the moving platform receiving coherent model, we can get:

In one embodiment, according to the moving platform transmitting coherent model and the moving platform receiving coherent model, a moving platform coherent calculation model is established as follows:

in,

Where r _l (n) is directly measured by radar.

In one embodiment, it is known that the transmission coherent parameters have a linear coupling relationship with r _l (n), l=1, 2, ..., N, that is, if r _l (n+1) can be predicted based on r _l (n), the transmission coherent parameters at time n+1 can be predicted.

According to the coupling relationship between the emission coherent parameters, the state vector of the emission coherent parameters is established as:

Where R[n] represents the state vector, r _l (n) represents the transmission coherent parameter;

The state vector is modeled using the Singer model:

where α is the inverse of the maneuver-related time constant, i.e., the maneuver frequency, is the acceleration variance of the maneuvering target;

The state equation is established as:

R[n+1]＝Φ(T,α)R[n]+u[n]

The driving noise covariance is:

In one embodiment, according to the state equation, the observation equation is determined as:

z[n]＝HX[n]+v[n]

Where, H = [100], v[n] = σ ² , σ ² is the radar measurement noise;

According to the observation equation, the Kalman filter equation is determined as:

P[n|n-1]＝ΦP[n|n]Φ ^T +Q[n]

K[n]＝P[n|n-1]H ^T (HP[n|n-1]H ^T +R) ^-1

P[n|n]＝(I-K[n]H)P[n|n-1]

In one embodiment, according to the Kalman filter equation, the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter is determined as:

According to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained as follows:

in,

Through the above embodiments, the beneficial effects of the present invention are as follows:

1. The method of the present invention applies the Kalman prediction filter to the coherent synthesis of the distributed radar of the moving platform, solves the problem of the coherent parameter recognition hysteresis in this case, and overcomes the deficiency of the method based on platform motion compensation that the moving speed of the target needs to be known.

2. The motion modeling of the present invention is based on the Singer model, which takes into account the possibility of all maneuvers of the target and can be applied to various types of maneuvers, with a wide range of application scenarios.

3. The method of the present invention has the characteristics of simple implementation, good stability and universality, and utilizes the characteristics of Kalman filtering and offline calculation gain to improve the real-time performance of the system.

The following is a description of the specific embodiments.

The present invention has been verified by simulation. The design simulation parameters are:

The simulation scenario is shown in Figure 4: The target trajectory has a high-maneuverability turn, which is used to test the performance of the algorithm in tracking high-maneuverability targets. The distance between each radar is 5m.

At the beginning, each radar transmits an orthogonal signal, and establishes a Kalman filter equation to filter and estimate the distance. At this time, due to the weak signal energy, the measurement error of the distance is large. From the distance error value of the first 20 seconds in Figure 5, it can be seen that the deviation is still relatively large. At the state switching moment, each radar transmits the same signal. At this time, the signal-to-noise ratio is improved, and the Kalman prediction filter based on the Singer model can better track the target. The maximum distance error in the target maneuvering stage is only 4m. Figure 6 shows the tracking result based on the basic Kalman prediction filter. It can be seen that the target measurement deviation increases significantly in the maneuvering stage. This proves the effectiveness of this method. Figures 7 and 8 are the comparisons of the predicted values and true values of the transmission coherent parameters of radar 2 and radar 3, respectively. It can be seen that the predicted values and true values are very close after the state switching moment.

It should be understood that, although the steps in the flowchart of FIG. 1 are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in FIG. 1 may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

In one embodiment, as shown in FIG9 , a coherent parameter acquisition system for a distributed coherent radar on a moving platform is provided, comprising: a scene modeling module 902, a Kalman filter module 904 and a coherent parameter calculation module 906, wherein:

The scene modeling module 902 is used to model the moving platform distributed coherent radar detection scene to obtain a moving platform transmission signal model, a moving platform transmission coherent model, and a moving platform reception coherent model; the moving platform distributed coherent radar detection scene is composed of a plurality of independent moving platforms, and the moving platforms are independent of each other; the moving platforms transmit data via wireless links;

The Kalman filter module 904 is used to establish a motion platform coherent calculation model according to the motion platform transmission coherent model and the motion platform reception coherent model; establish a state vector of the transmission coherent parameters according to the coupling relationship between the transmission coherent parameters, and use the Singer model to model the state vector to obtain a state equation of the transmission coherent parameters; determine an observation equation according to the state equation, and determine a Kalman filter equation according to the observation equation;

The coherent parameter calculation module 906 is used to determine the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter according to the Kalman filter equation, and obtain the coherent radar coherent parameters according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model.

In one embodiment, the scene modeling module 902 is also used to model the moving platform distributed coherent radar detection scene, and the moving platform transmission signal model of the m-th moving platform transmission signal is obtained as:

Where, _Tp is the transmit pulse width, u is the frequency modulation slope, rect(t) is the rectangular function, is the carrier, s _m (t) = exp(j2π(m-1)Δft);

m represents the number of the moving platform when transmitting the signal, and l represents the number of the moving platform when receiving the signal. Then the signal transmitted by the mth moving platform is:

Where κ _m represents the synchronization error of radar m compared to the reference clock, is the initial phase of radar m.

In one embodiment, the scene modeling module 902 is also used for the signal emitted by the m-th moving platform to reach the target. The signal is expressed as:

Where τ _m represents the arrival delay of the signal emitted by the mth moving platform, κ _m is the synchronization error of the radar relative to the reference clock, is the synchronization error of the radar relative to the reference phase;

The total signal reaching the target is:

Set radar 1 as the reference radar, then the adjusted transmission signals can be expressed as:

in and is the launch coherent parameter, and the launch coherent model of the moving platform is obtained as follows:

In one embodiment, the scene modeling module 902 is further configured to: if the l-th moving platform receives the target reflected echo, it is expressed as:

Where p(t) is the echo signal at the target;

Then the target echoes received by all radars are superimposed as:

Set radar 1 as the reference radar, and the adjusted received signals are expressed as:

in, and To receive the coherent parameters, the moving platform receiving coherent model is obtained as:

In one embodiment, the Kalman filter module 904 is further used to establish a motion platform coherence calculation model according to the motion platform transmission coherence model and the motion platform reception coherence model:

in,

Where r _l (n) is directly measured by radar.

In one embodiment, the Kalman filter module 904 is further configured to establish a state vector of the transmission coherent parameters according to the coupling relationship between the transmission coherent parameters:

Where R[n] represents the state vector, r _l (n) represents the transmission coherent parameter;

The state vector is modeled using the Singer model:

where α is the inverse of the maneuver-related time constant, i.e., the maneuver frequency, is the acceleration variance of the maneuvering target;

The state equation is established as:

R[n+1]＝Φ(T,α)R[n]+u[n]

The driving noise covariance is:

In one embodiment, the Kalman filter module 904 is further configured to determine the observation equation according to the state equation:

z[n]＝HX[n]+v[n]

Where, H = [100], v[n] = σ ² , σ ² is the radar measurement noise;

According to the observation equation, the Kalman filter equation is determined as:

P[n|n-1]＝ΦP[n|n]Φ ^T +Q[n]

K[n]＝P[n|n-1]H ^T (HP[n|n-1]H ^T +R) ^-1

P[n|n]＝(I-K[n]H)P[n|n-1]

In one embodiment, the coherent parameter calculation module 906 is further used to determine the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter according to the Kalman filter equation:

According to the predicted emission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained as follows:

in,

The specific definition of the moving platform distributed coherent radar coherent parameter acquisition system can be found in the definition of the moving platform distributed coherent radar coherent parameter acquisition method in the above text, which will not be repeated here. Each module in the above moving platform distributed coherent radar coherent parameter acquisition system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims (9)

1. A method for acquiring coherent parameters of a distributed coherent radar on a moving platform, characterized in that the method comprises: Modeling a moving platform distributed coherent radar detection scenario to obtain a moving platform transmission signal model, a moving platform transmission coherent model, and a moving platform reception coherent model; the moving platform distributed coherent radar detection scenario is composed of a plurality of independent moving platforms, and the moving platforms are independent of each other; the moving platforms transmit data via wireless links; Establishing a motion platform coherent calculation model according to the motion platform transmitting coherent model and the motion platform receiving coherent model; According to the coupling relationship between the emission coherent parameters, a state vector of the emission coherent parameters is established, and the state vector is modeled by using the Singer model to obtain a state equation of the emission coherent parameters; Determine an observation equation based on the state equation, and determine a Kalman filter equation based on the observation equation; According to the Kalman filter equation, a predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter is determined, and according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained. 2. The method according to claim 1 is characterized in that the step of modeling the moving platform distributed coherent radar detection scene to obtain the moving platform transmission signal model comprises: The moving platform distributed coherent radar detection scenario is modeled, and the moving platform transmission signal model of the mth moving platform transmission signal is obtained as follows: Where, _Tp is the transmit pulse width, u is the frequency modulation slope, rect(t) is the rectangular function, is the carrier, s _m (t) = exp(j2π(m-1)Δft); m represents the serial number of the motion platform when transmitting the signal, and l represents the serial number of the motion platform when receiving the signal; the signal transmitted by the mth motion platform is: Where κ _m represents the synchronization error of radar m compared to the reference clock, is the initial phase of radar m. 3. The method according to claim 2 is characterized in that the step of modeling the moving platform distributed coherent radar detection scene to obtain the moving platform emission coherent model comprises: The signal emitted by the mth moving platform and reaching the target is expressed as: Where τ _m represents the arrival delay of the signal emitted by the mth moving platform, κ _m is the synchronization error of the radar relative to the reference clock, is the synchronization error of the radar relative to the reference phase; The total signal reaching the target is: Set radar 1 as the reference radar, then the adjusted transmission signals can be expressed as: in and is the launch coherent parameter, and the launch coherent model of the moving platform is obtained as follows: . 4. The method according to claim 3 is characterized in that the step of modeling the moving platform distributed coherent radar detection scene to obtain the moving platform receiving coherent model comprises: If the lth moving platform receives the target reflected echo, it can be expressed as: Where p(t) is the echo signal at the target; Then the target echoes received by all radars are superimposed as: Set radar 1 as the reference radar, and the adjusted received signals are expressed as: in, and To receive the coherent parameters, the moving platform receiving coherent model is obtained as: . 5. The method according to any one of claims 1 to 4, characterized in that, according to the moving platform transmitting coherent model and the moving platform receiving coherent model, a moving platform coherent calculation model is established, comprising: According to the moving platform transmitting coherent model and the moving platform receiving coherent model, the moving platform coherent calculation model is established as follows: in, Where r _l (n) is directly measured by radar. 6. The method according to any one of claims 1 to 4, characterized in that the state vector of the transmission coherent parameters is established according to the coupling relationship between the transmission coherent parameters, and the state vector is modeled by using the Singer model to obtain the state equation of the transmission coherent parameters, comprising: According to the coupling relationship between the emission coherent parameters, the state vector of the emission coherent parameters is established as: Where R[n] represents the state vector, r _l (n) represents the transmission coherent parameter; The state vector is modeled using the Singer model: where α is the inverse of the maneuver-related time constant, i.e., the maneuver frequency, is the acceleration variance of the maneuvering target; The state equation is established as: R[n+1]＝Φ(T,α)R[n]+u[n] The driving noise covariance is: . 7. The method according to claim 6, characterized in that determining the observation equation according to the state equation, and determining the Kalman filter equation according to the observation equation, comprises: According to the state equation, the observation equation is determined as: z[n]＝HX[n]+v[n] Where, H = [100], v[n] = σ ² , σ ² is the radar measurement noise; According to the observation equation, the Kalman filter equation is determined as: P[n|n-1]＝ΦP[n|n]Φ ^T +Q[n] K[n]＝P[n|n-1]H ^T (HP[n|n-1]H ^T +R) ^-1 P[n|n]＝(I-K[n]H)P[n|n-1]. 8. The method according to claim 7 is characterized in that, according to the Kalman filter equation, a predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter is determined, and according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained, comprising: According to the Kalman filter equation, the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter is determined as: According to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model, the coherent radar coherent parameters are obtained as follows: in, 9. A coherent parameter acquisition system for a distributed coherent radar on a moving platform, characterized in that the system comprises: A scene modeling module is used to model a moving platform distributed coherent radar detection scene to obtain a moving platform transmission signal model, a moving platform transmission coherent model, and a moving platform reception coherent model; the moving platform distributed coherent radar detection scene is composed of a plurality of independent moving platforms, and the moving platforms are independent of each other; the moving platforms transmit data via wireless links; A Kalman filter module is used to establish a motion platform coherent calculation model according to the motion platform transmission coherent model and the motion platform reception coherent model; establish a state vector of the transmission coherent parameters according to the coupling relationship between the transmission coherent parameters, and use the Singer model to model the state vector to obtain a state equation of the transmission coherent parameters; determine an observation equation according to the state equation, and determine a Kalman filter equation according to the observation equation; The coherent parameter calculation module is used to determine the predicted transmission coherent parameter sequence corresponding to the transmission coherent parameter according to the Kalman filter equation, and obtain the coherent radar coherent parameters according to the predicted transmission coherent parameter sequence and the motion platform coherent calculation model.