CN113050053B - Method and system for acquiring phase parameters of distributed phase-coherent radar of mobile platform - Google Patents
Method and system for acquiring phase parameters of distributed phase-coherent radar of mobile platform Download PDFInfo
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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
Technical Field
The application relates to the technical field of signal processing, in particular to a method and a system for acquiring phase parameters of a distributed phase radar of a moving platform.
Background
In recent years, accidents caused by unmanned aerial vehicles are increasing, and research on effective detection technology aiming at targets of unmanned aerial vehicles in the low-altitude field is urgent. Because the target reflection area of the low-speed unmanned aerial vehicle is few, the condition of insufficient signal-to-noise ratio of the received echo of the single radar often exists, and the detection is difficult. The core idea of the distributed coherent radar is to enable multiple paths of transmitted signals to be overlapped at the target at the same time, so that the signal-to-noise ratio of an echo is larger than that of a single radar, and the detection capability of a small target is improved. The distributed coherent radar makes signals between the radars coherent by estimating a delay difference and a phase difference between the transmission and reception of the radars of the respective units, which is referred to as a coherent parameter (Coherent parameters, CPs). However, due to the maneuverability of the target drone, if a fixed platform distributed coherent radar approach is adopted, the target can easily fly outside the field of view of the radar. The distributed phase-coherent radar based on the movable platform has incomparable advantages of a fixed platform type distributed phase-coherent radar, and the unmanned aerial vehicle platform is taken as an example, so that the unmanned aerial vehicle has strong maneuvering capability, small terrain limitation and improved detection capability. However, the motion of the platform brings a plurality of challenges, and the hysteresis of the parameter estimation is an unavoidable problem for the practical trend of the distributed coherent radar of the moving platform, because when the relative motion exists between the target and the platform, the parameter perceived at the previous moment cannot be directly used for the coherent emission at the current moment, and the corresponding prediction method needs to be researched.
Disclosure of Invention
Based on the above, it is necessary to provide a method and a system for acquiring the parameters of the distributed coherent radar of the mobile platform, which cannot be realized by the distributed coherent radar of the mobile platform.
A method for acquiring parameters of a distributed coherent radar of a mobile platform, the method comprising:
modeling a dynamic platform distributed coherent radar detection scene to obtain a dynamic platform transmitting signal model, a dynamic platform transmitting coherent model and a dynamic platform receiving coherent model; the moving platform distributed coherent radar detection scene consists of a plurality of independent moving platforms, and the moving platforms are mutually independent; the data are transmitted between the motion platforms through wireless links;
according to the moving platform transmitting coherent model and the moving platform receiving coherent model, establishing a moving platform coherent calculation 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;
determining an observation equation according to the state equation, and determining a Kalman filtering equation according to the observation equation;
and determining a predicted emission phase parameter sequence corresponding to the emission phase parameter according to the Kalman filtering equation, and obtaining a phase parameter of the phase radar according to the predicted emission phase parameter sequence and the motion platform phase calculation model.
In one embodiment, the method further comprises: modeling a moving platform distributed coherent radar detection scene to obtain a moving platform emission signal model of an mth moving platform emission signal, wherein the moving platform emission signal model is as follows:
wherein T is p For the transmit pulse width, u is the frequency modulation slope, rect (t) is a rectangular function,is carrier wave, s m (t)＝exp(j2π(m-1)Δft)；
M represents the serial number of the moving platform when transmitting signals, and l represents the serial number of the moving platform when receiving signals. The signal transmitted by the mth motion platform is:
middle kappa m Representing the synchronization error of the radar m with respect to the reference clock,is the initial phase of radar m.
In one embodiment, the method further comprises: the signal that the m-th motion platform transmitted reaches the target is expressed as:
wherein τ m Represents the time delay, κ, of arrival of the signal transmitted by the mth motion platform m For the synchronization error of the radar compared to the reference clock,a synchronization error of the radar relative to a reference phase;
the total signal arriving at the target is:
setting the radar 1 as the reference radar, each of the adjusted transmit signals may be expressed as:
wherein the method comprises the steps ofAnd->For transmitting the phase parameter, the obtained dynamic platform transmitting phase parameter model is as follows:
in one embodiment, the method further comprises: if the first motion platform receives the target reflection echo, the method is as follows:
where p (t) is the echo signal at the target;
all radar received target echoes are superimposed as:
setting the radar 1 as a reference radar, each adjusted received signal is expressed as:
wherein,,and->In order to receive the parameter, the dynamic platform receives the parameter model as follows:
in one embodiment, the method further comprises: according to the moving platform transmitting phase parameter model and the moving platform receiving phase parameter model, a moving platform phase parameter calculation model is established as follows:
wherein,,
wherein r is l (n) is measured directly by radar.
In one embodiment, the method further comprises: according to the coupling relation between the emission phase parameters, the state vector of the emission phase parameters is established as follows:
wherein R < n >]Representing a state vector, r l (n) represents an emission-related parameter;
modeling the state vector by adopting a Singer model as follows:
where a is the inverse of the maneuver-related time constant, i.e. the maneuver frequency,acceleration variance, which is a maneuver target;
the state equation is established as follows:
R[n+1]＝Φ(T,α)R[n]+u[n]
the driving noise covariance is:
in one embodiment, the method further comprises: according to the state equation, determining an observation equation as follows:
z[n]＝HX[n]+v[n]
wherein H= [10]，v[n]＝σ 2 ，σ 2 Measuring noise for the radar;
according to the observation equation, determining a Kalman filtering equation as follows:
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 method further comprises: according to the Kalman filtering equation, determining a predicted emission phase parameter sequence corresponding to the emission phase parameter as follows:
according to the predicted emission coherent parameter sequence and the motion platform coherent computation model, obtaining coherent radar coherent parameters as follows:
wherein,,
a mobile platform distributed coherent radar coherent parameter acquisition system, the system comprising:
the scene modeling module is used for 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; the moving platform distributed coherent radar detection scene consists of a plurality of independent moving platforms, and the moving platforms are mutually independent; the data are transmitted between the motion platforms through wireless links;
the Kalman filtering module is used for establishing a motion platform phase-parameter calculation model according to the motion platform transmitting phase-parameter model and the motion platform receiving phase-parameter 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; determining an observation equation according to the state equation, and determining a Kalman filtering equation according to the observation equation;
and the coherent parameter calculation module is used for determining a predicted emission coherent parameter sequence corresponding to the emission coherent parameter according to the Kalman filtering equation, and obtaining the coherent radar coherent parameter according to the predicted emission coherent parameter sequence and the motion platform coherent calculation model.
According to the method and the system for acquiring the parameters of the distributed type phase-coherent radar of the moving platform, the parameters of the transmitted phase-coherent radar of the moving platform and the coupling relation between different platforms and the distance between the targets are utilized, the distance between the targets and the platform is measured, the distance change sequence is acquired, a Kalman filter is designed to conduct one-step prediction on the distance change sequence, the parameters of the transmitted phase-coherent radar of the next moment are estimated according to the predicted distance value, and research support is provided for acquiring the parameters of the distributed type phase-coherent radar of the moving platform and designing the system.
Drawings
FIG. 1 is a flow chart of a method for acquiring parameters of a moving platform distributed coherent radar in one embodiment;
FIG. 2 is a schematic diagram of a motion platform detecting an object in one embodiment;
FIG. 3 is a schematic diagram of a parameter estimation process according to an embodiment;
FIG. 4 is a diagram of a dynamic platform distributed coherent radar simulation scenario in one embodiment;
FIG. 5 is a graph of range error of radar measurements (based on the singer model) in one embodiment;
FIG. 6 is a diagram of measured distance errors for each radar (without singer model) in one embodiment
FIG. 7 is a graph of radar 2 coherent parameter error variation in one embodiment;
FIG. 8 is a graph of radar 3 coherent parameter error variation in one embodiment;
fig. 9 is a block diagram of a dynamic platform distributed coherent radar coherent parameter acquisition system according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a method for obtaining Xiang Can parameters of a moving platform distributed coherent radar is provided, which includes the following steps:
step 102, modeling a mobile platform distributed coherent radar detection scene to obtain a mobile platform transmitting signal model, a mobile platform transmitting coherent model and a mobile platform receiving coherent model.
The moving platform distributed coherent radar can be composed of a plurality of moving platforms, the moving platforms are mutually independent, and data can be transmitted through a wireless link, so that various working modes can be selected. In the MIMO mode, each motion platform transmits orthogonal signals while each motion platform receives signals. Because the transmitted orthogonal signals are orthogonal signals, echoes transmitted by different motion platforms can be separated by filtering and other modes at the receiving motion platform end, then signals of a certain channel are selected as reference signals, signals of the other channels are aligned with the reference signals in terms of time delay and phase, and the receiving phase correlation is completed. After the accurate phase parameter is obtained through the MIMO mode, the system can be switched to a full-phase working mode, and the system controls the time and the initial phase of signals transmitted by different motion platforms in the full-phase working mode, so that the signals transmitted by all the motion platforms reach the target at the same time and in the same phase, one-time superposition is formed at the target, the transmission phase parameter is achieved, and the superimposed echo is processed on the basis. Two modes of operation of the mobile platform distributed coherent system are discussed below. When the system works in the MIMO mode, echoes of signals transmitted by different motion platforms need to be separated, so that orthogonal signals are needed, and orthogonal frequency division signal transmission waveforms are adopted.
And 104, building a motion platform coherent computing model according to the motion platform transmitting coherent model and the motion platform receiving coherent model.
Because the distance walk generated by the relative motion of the moving platform and the target is not negligible, the phase parameters are also changed along with the distance walk, and the change is mainly caused by the change of the path difference. The purpose of the emission phase correlation is to generate forward interference at the target, but because of the real-time change of the relative positions of the platform and the target and the phase correlation parameter cognition always occurs before the phase correlation synthesis, the method means that the emission phase correlation parameter cognition under one observation can not be corrected to carry out phase correlation, namely the cognition knowledge has hysteresis (the consideration of time and phase synchronization errors is unchanged), and in a simple way, the method can be directly measured. The detection scenario is shown in fig. 2, and in the platform (target) motion scenario, reasonable prediction of the emission correlation parameter is necessary to compensate for the decoherence caused by the cognitive recognition lag. In short, the transmission parameter at time n needs to be determined according to the state at time n, but the latest state obtained by measurement is still at time n-1.
And 106, establishing a state vector of the emission parameter according to the coupling relation between the emission parameter and the parameter, and modeling the state vector by adopting a Singer model to obtain a state equation of the emission parameter.
Step 108, determining an observation equation according to the state equation, and determining a Kalman filtering equation according to the observation equation.
Step 110, determining a predicted emission phase parameter sequence corresponding to the emission phase parameter according to a Kalman filtering equation, and obtaining a phase radar phase parameter according to the predicted emission phase parameter sequence and a motion platform phase calculation model.
According to the method for acquiring the parameters of the distributed coherent radar of the moving platform, the parameters of the transmitted coherent radar of the moving platform are utilized, the distance between the target and the platform is measured, the distance change sequence is obtained, the Kalman filter is designed to conduct one-step prediction on the distance change sequence, the parameters of the transmitted coherent radar of the next moment are estimated according to the predicted distance value, and research support is provided for acquiring the parameters of the radar of the distributed coherent radar of the moving platform and designing a system.
In one embodiment, modeling a moving platform distributed coherent radar detection scene to obtain a moving platform emission signal model of an mth moving platform emission signal, where the moving platform emission signal model is:
wherein T is p For the transmit pulse width, u is the frequency modulation slope, rect (t) is a rectangular function,is carrier wave, s m (t)＝exp(j2π(m-1)Δft)；
M represents the serial number of the moving platform when transmitting signals, and l represents the serial number of the moving platform when receiving signals. The signal transmitted by the mth motion platform is:
middle kappa m Representing the synchronization error of the radar m with respect to the reference clock,is the initial phase of radar m.
In one embodiment, the signal that the mth motion platform transmits to reach the target is expressed as:
wherein τ m Represents the time delay, κ, of arrival of the signal transmitted by the mth motion platform m For the synchronization error of the radar compared to the reference clock,a synchronization error of the radar relative to a reference phase;
the total signal arriving at the target is:
setting the radar 1 as the reference radar, each of the adjusted transmit signals may be expressed as:
wherein the method comprises the steps ofAnd->For transmitting the phase parameter, the obtained dynamic platform transmitting phase parameter model is as follows:
in one embodiment, if the first motion platform receives the target reflection echo, the method is as follows:
where p (t) is the echo signal at the target;
all radar received target echoes are superimposed as:
setting the radar 1 as a reference radar, each adjusted received signal is expressed as:
wherein,,and->In order to receive the parameter, the dynamic platform receives the parameter model as follows:
obviously, at the target, the signals received by each platform cannot be superimposed in the same direction, and in order to realize coherent superposition of electromagnetic wave energy, namely receiving phase correlation, the receiving delay and the receiving phase of each radar need to be adjusted.
In one embodiment, assuming that the target and the platform can be approximately considered stationary in a single pulse, the coherent parameters cannot be directly measured by the coherent model transmitted by the moving platform and the coherent model received by the moving platform, in order to estimate the coherent parameters, each radar is required to transmit orthogonal signals, the receiving end separates self-received echo and self-received echo by matching filtering, and the separated echo is recorded as
Wherein τ lm ＝τ l +τ m +κ m -κ l Represents the time delay alpha of the signal transmitted by the mth motion platform reaching the first motion platform after being reflected by the target lm For the response of the scatterer on this path,is the phase synchronization error of the two radars.
The parameter estimation flow chart is shown in fig. 3, and can be obtained by definition of parameter:
when the system transmits the same waveform, the receiving end can still obtain the receiving parameter, but can not obtain the transmitting parameter again, so that the conversion relation between the transmitting parameter and the receiving parameter must be established, and the receiving parameter model of the moving platform can be obtained by comparing the transmitting parameter model of the moving platform with the receiving parameter model of the moving platform:
in one embodiment, according to the moving platform transmitting and receiving coherent models, the moving platform coherent computing model is established as follows:
wherein,,
wherein r is l (n) is measured directly by radar.
In one embodiment, the transmit correlation parameter is known to be r l (N), l=1, 2, …, N has a linear coupling relationship, i.e. if it can be determined according to r l (n) predicting r l The emission coherent parameter at the time of n+1 can be predicted by (n+1).
According to the coupling relation between the emission phase parameters, the state vector of the emission phase parameters is established as follows:
wherein R < n >]Representing a state vector, r l (n) represents an emission-related parameter;
modeling the state vector by adopting a Singer model as follows:
where a is the inverse of the maneuver-related time constant, i.e. the maneuver frequency,acceleration variance, which is a maneuver target;
the state equation is established as follows:
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]
wherein H= [100 ]]，v[n]＝σ 2 ，σ 2 Measuring noise for the radar;
according to the observation equation, determining a Kalman filtering equation as follows:
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 emission phase parameter sequence corresponding to the emission phase parameter is determined as:
according to the predicted emission coherent parameter sequence and the motion platform coherent computation model, the coherent radar coherent parameters are obtained as follows:
wherein,,
through the embodiment, the beneficial effects of the invention are as follows:
1. the method of the invention applies the Kalman prediction filter to the coherent synthesis of the moving platform distributed radar, solves the problem of coherent parameter cognitive hysteresis under the condition, and overcomes the defect that the motion speed of a known target is required by a platform-based motion compensation method.
2. The motion modeling of the invention is based on a Singer model, considers the possibility of all maneuvering of the target, can be applied to various maneuvering types, and has wider application scenes.
3. The method has the characteristics of simplicity in implementation, good stability, universality and the like, and the real-time performance of the system is improved by utilizing the characteristics of Kalman filtering and off-line gain calculation.
Specific examples are described below.
The present invention has been verified through simulation. The design simulation parameters are as follows:
the simulation scenario is as shown in fig. 4: the target track is a motor turn with a section height, so that the performance of the algorithm for tracking the motor target with the section height is detected. The distance between each two radars is 5m.
In the beginning stage, each radar transmits orthogonal signals, a Kalman filter equation is established at the same time, filtering estimation is carried out on the distance, at the moment, because the signal energy is weak, the measurement error of the distance is larger, whether the deviation is larger or not can be seen from the distance error value of the front 20s in fig. 5, at the moment of state switching, each radar transmits the same signal, the signal to noise ratio is improved, a Kalman prediction filter based on a singer model can be used for better tracking a target, the maximum distance error of the target is 4m only in a maneuvering section of the target, and the tracking result based on the basic Kalman prediction filter is shown in fig. 6, so that the measurement deviation of the target in the maneuvering section can be obviously increased. The effectiveness of the method is demonstrated. Fig. 7 and 8 are a comparison of predicted and actual values of the transmitted coherent parameters of radar 2 and radar 3, respectively, and it can be seen that the predicted and actual values are very close together also after the state switching instant.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be performed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, or the order in which the sub-steps or stages are performed is not necessarily sequential, but may be performed in rotation or alternatively with at least a portion of the sub-steps or stages of other steps or steps.
In one embodiment, as shown in fig. 9, there is provided a mobile platform distributed coherent radar Xiang Can parameter acquisition system comprising: a scene modeling module 902, a kalman filtering module 904, and a coherent parameter calculation module 906, wherein:
the scene modeling module 902 is configured to model a moving platform distributed coherent radar detection scene to obtain a moving platform emission signal model, a moving platform emission coherent model, and a moving platform receiving coherent model; the moving platform distributed coherent radar detection scene consists of a plurality of independent moving platforms, and the moving platforms are mutually independent; the data are transmitted between the motion platforms through wireless links;
the kalman filtering module 904 is configured to establish a motion platform phase parameter calculation model according to the motion platform transmitting phase parameter model and the motion platform receiving phase parameter 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; determining an observation equation according to the state equation, and determining a Kalman filtering equation according to the observation equation;
and the coherent parameter calculation module 906 is configured to determine a predicted transmit coherent parameter sequence corresponding to the transmit coherent parameter according to the kalman filter equation, and obtain a coherent radar coherent parameter according to the predicted transmit coherent parameter sequence and the motion platform coherent calculation model.
In one embodiment, the scene modeling module 902 is further configured to model a moving platform distributed coherent radar detection scene, where the moving platform emission signal model for obtaining the mth moving platform emission signal is:
wherein T is p For the transmit pulse width, u is the frequency modulation slope, rect (t) is a rectangular function,is carrier wave, s m (t)＝exp(j2π(m-1)Δft)；
M represents the serial number of the moving platform when transmitting signals, and l represents the serial number of the moving platform when receiving signals. The signal transmitted by the mth motion platform is:
middle kappa m Representing the synchronization error of the radar m with respect to the reference clock,is the initial phase of radar m.
In one embodiment, the scene modeling module 902 is further configured to represent the signal emitted by the mth motion platform reaching the target as:
wherein τ m Represents the time delay, κ, of arrival of the signal transmitted by the mth motion platform m For the synchronization error of the radar compared to the reference clock,for the purpose ofThe synchronization error of the radar compared with the reference phase;
the total signal arriving at the target is:
setting the radar 1 as the reference radar, each of the adjusted transmit signals may be expressed as:
wherein the method comprises the steps ofAnd->For transmitting the phase parameter, the obtained dynamic platform transmitting phase parameter model is as follows:
in one embodiment, the scene modeling module 902 is further configured to, if the first motion platform receives the target reflected echo representation as:
where p (t) is the echo signal at the target;
all radar received target echoes are superimposed as:
setting the radar 1 as a reference radar, each adjusted received signal is expressed as:
wherein,,and->In order to receive the parameter, the dynamic platform receives the parameter model as follows:
in one embodiment, the kalman filtering module 904 is further configured to establish a motion platform coherent computation model according to the motion platform transmit coherent model and the motion platform receive coherent model, where the motion platform coherent computation model is:
wherein,,
wherein r is l (n) is measured directly by radar.
In one embodiment, the kalman filter module 904 is further configured to establish a state vector of the transmission parameter according to a coupling relationship between the transmission parameter as follows:
wherein R < n >]Representing a state vector, r l (n) represents an emission-related parameter;
modeling the state vector by adopting a Singer model as follows:
where a is the inverse of the maneuver-related time constant, i.e. the maneuver frequency,acceleration variance, which is a maneuver target;
the state equation is established as follows:
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, according to the state equation, an observation equation as:
z[n]＝HX[n]+v[n]
wherein H= [100 ]]，v[n]＝σ 2 ，σ 2 Measuring noise for the radar;
according to the observation equation, determining a Kalman filtering equation as follows:
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 configured to determine, according to the kalman filter equation, a predicted transmit coherent parameter sequence corresponding to the transmit coherent parameter as follows:
according to the predicted emission coherent parameter sequence and the motion platform coherent computation model, obtaining coherent radar coherent parameters as follows:
wherein,,
the specific limitation of the mobile platform distributed coherent radar coherent parameter acquisition system can be referred to above, and the description of the limitation of the mobile platform distributed coherent radar coherent parameter acquisition method is omitted here. All or part of each module in the dynamic platform distributed coherent radar coherent parameter acquisition system can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the spirit of the present application, which falls within the scope of the present application. Accordingly, the scope of protection of the present application is subject to the appended claims.
Claims (9)
1. The method for acquiring the phase parameters of the distributed phase-coherent radar of the moving platform is characterized by comprising the following steps:
modeling a dynamic platform distributed coherent radar detection scene to obtain a dynamic platform transmitting signal model, a dynamic platform transmitting coherent model and a dynamic platform receiving coherent model; the moving platform distributed coherent radar detection scene consists of a plurality of independent moving platforms, and the moving platforms are mutually independent; the data are transmitted between the motion platforms through wireless links;
according to the moving platform transmitting coherent model and the moving platform receiving coherent model, establishing a moving platform coherent calculation 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;
determining an observation equation according to the state equation, and determining a Kalman filtering equation according to the observation equation;
and determining a predicted emission phase parameter sequence corresponding to the emission phase parameter according to the Kalman filtering equation, and obtaining the phase parameter of the phase radar according to the predicted emission phase parameter sequence and the phase calculation model of the motion platform.
2. The method of claim 1, wherein modeling the moving platform distributed coherent radar detection scenario to obtain a moving platform transmit signal model comprises:
modeling a moving platform distributed coherent radar detection scene to obtain a moving platform emission signal model of an mth moving platform emission signal, wherein the moving platform emission signal model is as follows:
wherein T is p For the transmit pulse width, u is the frequency modulation slope, rect (t) is a rectangular function,is carrier wave, s m (t)＝exp(j2π(m-1)Δft)；
M represents the serial number of the moving platform when transmitting signals, and l represents the serial number of the moving platform when receiving signals, and the signal transmitted by the mth moving platform is:
middle kappa m Representing the synchronization error of the radar m with respect to the reference clock,is the initial phase of radar m.
3. The method according to claim 2, wherein modeling the moving platform distributed coherent radar detection scene to obtain a moving platform emission coherent model comprises:
the signal that the m-th motion platform transmitted reaches the target is expressed as:
wherein τ m Represents the time delay, κ, of arrival of the signal transmitted by the mth motion platform m For the synchronization error of the radar compared to the reference clock,a synchronization error of the radar relative to a reference phase;
the total signal arriving at the target is:
setting the radar 1 as the reference radar, each of the adjusted transmit signals may be expressed as:
wherein the method comprises the steps ofAnd->For transmitting the phase parameter, the obtained dynamic platform transmitting phase parameter model is as follows:
。
4. a method according to claim 3, wherein modeling the moving platform distributed coherent radar detection scenario to obtain a moving platform received coherent model comprises:
if the first motion platform receives the target reflection echo, the method is as follows:
where p (t) is the echo signal at the target;
all radar received target echoes are superimposed as:
setting the radar 1 as a reference radar, each adjusted received signal is expressed as:
wherein,,and->In order to receive the parameter, the dynamic platform receives the parameter model as follows:
。
5. the method according to any one of claims 1 to 4, wherein building a motion platform coherent computing model from the motion platform transmit coherent model and the motion platform receive coherent model comprises:
according to the moving platform transmitting coherent model and the moving platform receiving coherent model, establishing a moving platform coherent computing model as follows:
wherein,,
wherein r is l (n) is measured directly by radar.
6. The method according to any one of claims 1 to 4, wherein the establishing a state vector of the emission parameter according to the coupling relation between the emission parameter, and modeling the state vector with a Singer model to obtain a state equation of the emission parameter, includes:
according to the coupling relation between the emission phase parameters, the state vector of the emission phase parameters is established as follows:
wherein R < n >]Representing a state vector, r l (n) represents an emission-related parameter;
modeling the state vector by adopting a Singer model as follows:
where a is the inverse of the maneuver-related time constant, i.e. the maneuver frequency,acceleration variance, which is a maneuver target;
the state equation is established as follows:
R[n+1]＝Φ(T,α)R[n]+u[n]
the driving noise covariance is:
。
7. the method of claim 6, wherein determining an observation equation from the state equation and determining a kalman filter equation from the observation equation comprises:
according to the state equation, determining an observation equation as follows:
z[n]＝HX[n]+v[n]
wherein H= [100 ]]，v[n]＝σ 2 ，σ 2 Measuring noise for the radar;
according to the observation equation, determining a Kalman filtering equation as follows:
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 of claim 7, wherein determining a predicted transmit phase parameter sequence corresponding to a transmit phase parameter according to the kalman filter equation, and obtaining a phase radar phase parameter according to the predicted transmit phase parameter sequence and the motion platform phase calculation model, comprises:
according to the Kalman filtering equation, the predicted emission phase parameter sequence corresponding to the emission phase parameter is determined as follows:
according to the predicted emission coherent parameter sequence and the motion platform coherent computation model, obtaining coherent radar coherent parameters as follows:
wherein,,
9. a mobile platform distributed coherent radar coherent parameter acquisition system, the system comprising:
the scene modeling module is used for 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; the moving platform distributed coherent radar detection scene consists of a plurality of independent moving platforms, and the moving platforms are mutually independent; the data are transmitted between the motion platforms through wireless links;
the Kalman filtering module is used for establishing a motion platform phase parameter calculation model according to the motion platform transmitting phase parameter model and the motion platform receiving phase parameter 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; determining an observation equation according to the state equation, and determining a Kalman filtering equation according to the observation equation;
and the coherent parameter calculation module is used for determining a predicted emission coherent parameter sequence corresponding to the emission coherent parameter according to the Kalman filtering equation, and obtaining the coherent radar coherent parameter according to the predicted emission coherent parameter sequence and the motion platform coherent calculation model.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102207548A (en) * | 2010-03-31 | 2011-10-05 | 中国科学院电子学研究所 | MIMO SAR imaging method by employing minimum mean square error estimation |
| CN109188387A (en) * | 2018-08-31 | 2019-01-11 | 西安电子科技大学 | Distributed coherent radar target component estimation method based on Interpolation compensation |
| CN110907910A (en) * | 2019-11-27 | 2020-03-24 | 中国船舶重工集团公司第七二四研究所 | Distributed coherent radar moving target echo coherent synthesis method |
| EP3712652A1 (en) * | 2019-03-18 | 2020-09-23 | NXP USA, Inc. | Distributed aperture automotive radar system |
| CN112130139A (en) * | 2020-08-21 | 2020-12-25 | 西安空间无线电技术研究所 | Distributed full-coherent sparse linear array radar system optimization array arrangement method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112415480A (en) * | 2019-08-20 | 2021-02-26 | 是德科技股份有限公司 | Multiple-input multiple-output (MIMO) target simulation system and method for testing millimeter wave radar sensor |
-
2021
- 2021-03-17 CN CN202110289756.2A patent/CN113050053B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102207548A (en) * | 2010-03-31 | 2011-10-05 | 中国科学院电子学研究所 | MIMO SAR imaging method by employing minimum mean square error estimation |
| CN109188387A (en) * | 2018-08-31 | 2019-01-11 | 西安电子科技大学 | Distributed coherent radar target component estimation method based on Interpolation compensation |
| EP3712652A1 (en) * | 2019-03-18 | 2020-09-23 | NXP USA, Inc. | Distributed aperture automotive radar system |
| CN110907910A (en) * | 2019-11-27 | 2020-03-24 | 中国船舶重工集团公司第七二四研究所 | Distributed coherent radar moving target echo coherent synthesis method |
| CN112130139A (en) * | 2020-08-21 | 2020-12-25 | 西安空间无线电技术研究所 | Distributed full-coherent sparse linear array radar system optimization array arrangement method |
Non-Patent Citations (3)
| Title |
|---|
| Using platform motion for improved spatial filtering in distributed antenna arrays;CHATTERJEE P et al.;2018 IEEE Radio and Wireless Symposium. Anaheim;全文 * |
| 动平台分布式相参雷达系统分析;卢佳欣 等;信号处理;第35卷(第5期);全文 * |
| 基于多特显点的无人机分布式相参雷达相位同步误差估计方法;刘晓瑜;吴建新;王彤;陈金铭;;系统工程与电子技术(04);全文 * |
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