CN110824432B - Radar clutter suppression method, device and computer readable storage medium - Google Patents
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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
- G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
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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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
- G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
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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
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—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
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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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
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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Abstract
The invention discloses a radar clutter suppression method, a device and a computer readable storage medium, wherein the radar clutter suppression method comprises the following steps: step S10: acquiring an echo signal received by a radar; wherein, the total reflected signals received by the radar are defined as echo signals
Wherein the content of the first and second substances,
the slow time dimension is represented as the kth frame, and the fast time dimension is represented as
A signal of a time of day; step S20: estimating a clutter signal from the echo signal; step S201: and dynamically adjusting the weight parameter in the exponential moving average algorithm, defining the weight parameter as a forgetting factor lambda, and then updating the forgetting factor lambda of the kth frame. The technical scheme provided by the invention mainly aims to iteratively update the forgetting factor lambda of the exponential moving average algorithm by constructing the cost function and setting the function value, so that the lambda can automatically select a larger value in a balanced environment to improve the balanced state performance, and automatically select the larger value when the signal environment changes suddenlySmall to increase convergence speed.
Description
Technical Field
The present invention relates to the technical field of clutter suppression, and in particular, to a radar clutter suppression method, apparatus, and computer-readable storage medium.
Background
Clutter suppression has long been an indispensable loop for radar signal processing, both in military and civilian applications. The effective clutter suppression can filter out a lot of signal interference, thereby laying a foundation for subsequent signal processing such as target detection, feature extraction and the like. Radar clutter suppression methods can be divided into single antenna radar and multiple antenna radar methods. As a starting stage, the single-antenna radar has a plurality of algorithms which lay the foundation, such as Moving Target Indicator (MTI), moving Target Detector (MTD), pulse Doppler (pulse Doppler) processing, and the like. As one representative example of the MTI algorithm, background subtraction method combining Exponential Moving Average (EMA) is a relatively common radar clutter suppression method, which estimates and updates a clutter signal at each time in an Exponential Average form and then subtracts the clutter signal. Compared with other algorithms, the EMA algorithm is simple to calculate, and clutter suppression performance can meet the daily task requirements of the single-antenna radar. The clutter environment faced by the multi-antenna radar is relatively complex, and most of the clutter environment is a time-varying environment, so that the research on the clutter suppression method of the multi-antenna radar is relatively deep. The beam forming technique is one of the representative techniques, and performs weighting processing on signals received by each array element antenna to suppress clutter and interference signals in the space domain. In order to make the system respond to environmental changes accurately and in time, researchers are constantly developing beam forming algorithms that improve adaptation, such as Blind Adaptive algorithms (Blind Adaptive Algorithm), least Mean Square (LMS) algorithms, recursive Least Square (RLS) algorithms, and so on. The RLS adaptive algorithm introduces an exponential weighting factor, the forgetting factor, to control the time the system "forgets" past data and the rate at which the current signal is tracked. For the adjustment of the Variable Forgetting Factor, researchers also propose a plurality of improved algorithms, such as a traditional Gradient Variable Forgetting Factor (GVFF) adjustment algorithm, and a Time-Averaged Variable Forgetting Factor (TAVFF) adjustment algorithm. The algorithm is mainly applied to multi-antenna radars.
Because the clutter environment of the multi-antenna radar is relatively complex, the calculation complexity of the clutter suppression algorithm is generally higher, but the performance is better, while the application environment of the single-antenna radar is generally simpler, and the static clutter occupies a main position. Therefore, the EMA algorithm is simple in structure and meets the requirements of people, but the EMA algorithm with the fixed forgetting factor cannot adaptively suppress clutter signals in a time-varying environment. In recent years, some scholars also propose some adaptive algorithms to adjust the forgetting factor of the EMA algorithm, but the scholars do not design the algorithm aiming at the time-varying environment; therefore, how to suppress clutter signals in a time-varying environment is an urgent problem to be solved.
Disclosure of Invention
The invention provides a radar clutter suppression method, a radar clutter suppression device and a computer readable storage medium, and mainly aims to construct a cost function and set a function value to iteratively update a forgetting factor lambda of an exponential moving average algorithm, so that the lambda can automatically select a large value in a balanced environment to improve balanced state performance, and automatically select a small value when a signal environment suddenly changes to improve convergence speed.
In order to achieve the above object, the present invention provides a radar clutter suppression method, apparatus and computer readable storage medium, wherein the radar clutter suppression method comprises the following steps:
a radar clutter suppression method is characterized by comprising the following steps: the method comprises the following steps:
step S10: acquiring an echo signal received by a radar;
wherein all reflected signals received by the radar are defined as echo signals
Wherein the content of the first and second substances,
indicating a slow time dimension as the kth frame and a fast time dimension as
A signal of a time of day;
step S20: estimating a clutter signal from the echo signal;
step S201: dynamically adjusting a weight parameter in an exponential moving average algorithm, defining the weight parameter as a forgetting factor lambda, and then updating the forgetting factor lambda of the kth frame;
wherein the value of k is more than or equal to 1;
step S202: estimating the value of the clutter signal frame by updating the filter of the exponential moving average algorithm of the forgetting factor λ of the kth frame
The following formula (1):
wherein k represents the kth frame, λ represents a forgetting factor, and λ is greater than or equal to 0 and less than or equal to 1;
step S30: from the echo signal
Iteratively calculating the suppressed output signal
The following formula (2) represents:
step S40: returning to step S20, the clutter signals of the k +1 frame are estimated from the suppressed output signal of the k-th frame.
Optionally, the dynamically adjusting the weight parameter in the exponential moving average algorithm in step S201 includes:
step S2011: according to the suppressed output signal
Performing a transformation and establishing a cost function, wherein the cost function is defined as the suppressed output signal
The square value of (a) is expressed by the following formula (3):
step S2012: and iteratively updating the forgetting factor lambda according to the function values of the cost function at different moments.
Optionally, step S2012 includes:
step S2013: obtaining function values of the cost function at different moments, calculating a weighted sum of the cost function values at the past moment and the current moment, wherein the definition is used for calculating the weighted sum
For the weighted summation of the cost function values at the past time and the current time, the following formula (4) represents:
step S2014: according to
Iteratively updating the forgetting factor λ by the function value of (a), as represented by the following equation (5):
wherein the content of the first and second substances,
indicates that the forgetting factor value is limited to [ lambda ] min ,λ max ]In, α, β are self-definedThe parameter (c) of (c).
Optionally, the echo signal in step S10 includes a target signal, a clutter signal and a noise signal.
Optionally, in the step S2011, the cost function is the output signal
Such that when the amplitude of a certain distance unit of the frame abruptly changes, the cost function value increases abruptly; when the amplitude of a certain distance unit in the frame keeps stable, the cost function value approaches zero.
In order to achieve the above object, the present invention further provides a radar clutter suppression apparatus, including a memory and a processor, wherein the memory stores a radar clutter suppression program operable on the processor, the radar clutter suppression program is based on a combined exponential moving average algorithm, and the radar clutter suppression program, when executed by the processor, implements the steps of the above radar clutter suppression method.
To achieve the above object, the present invention also provides a computer-readable storage medium having a radar clutter suppression program stored thereon, the radar clutter suppression program being executable by one or more processors to implement the steps of the radar clutter suppression method described above.
The radar clutter suppression method, the radar clutter suppression device and the computer readable storage medium mainly aim at enabling lambda to automatically select a large value in a balanced environment to improve balanced state performance and automatically select a small value when a signal environment changes suddenly to improve convergence rate by constructing a cost function and setting a function value to iteratively update a forgetting factor lambda of an exponential moving average algorithm.
Drawings
Fig. 1 is a schematic flowchart of a radar clutter suppression method according to an embodiment of the present invention;
FIG. 2 is a graph showing a comparison relationship between the IF of the radar clutter suppression method and the IF of other algorithms varying with the frame number according to an embodiment of the present invention;
FIG. 3 is a signal amplitude diagram of a 1500 th frame without clutter suppression according to an embodiment of the present invention;
fig. 4 is a signal amplitude diagram of a 1500 th frame processed by the EMA algorithm with λ =0 according to an embodiment of the present invention;
fig. 5 is a signal amplitude diagram of a 1500 th frame processed by the EMA algorithm with λ =0.7 according to an embodiment of the present invention;
FIG. 6 is a signal magnitude plot of a 1500 th frame processed by the Yoo's algorithm provided by an embodiment of the present invention;
FIG. 7 is a signal amplitude diagram of a 1500 th frame processed by a radar clutter suppression method according to an embodiment of the present invention;
FIG. 8 is a signal amplitude diagram of a 1800 th frame without clutter suppression according to an embodiment of the present invention;
fig. 9 is a signal amplitude diagram of a 1800 th frame processed by the EMA algorithm with λ =0 according to an embodiment of the present invention;
fig. 10 is a signal amplitude diagram of an 1800 th frame processed by the EMA algorithm with λ =0.7 according to an embodiment of the present invention;
FIG. 11 is a signal magnitude plot of a 1800 th frame processed by the Yoo's algorithm provided by an embodiment of the present invention;
fig. 12 is a signal amplitude diagram of an 1800 th frame processed by a radar clutter suppression method according to an embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, the present invention provides a radar clutter suppression method, apparatus and computer readable storage medium, wherein the radar clutter suppression method comprises the following steps:
step S10: acquiring an echo signal received by a radar; wherein, the total reflected signals received by the radar are defined as echo signals, and the signals reflected by other objects except the target received by the radar are defined as clutterA signal; representing the received signal as
Representing clutter signals as
Wherein the content of the first and second substances,
the slow time dimension is represented as the kth frame, and the fast time dimension is represented as
A signal of a time of day;
specifically, the clutter signals in step S10 further include noise signals, which are generally assumed to be stationary and are also suppressed along with the clutter when the clutter signals are suppressed;
step S20: estimating a clutter signal from the echo signal;
step S201: dynamically adjusting a weight parameter in an exponential moving average algorithm, defining the weight parameter as a forgetting factor lambda, and then updating the forgetting factor lambda of the kth frame;
wherein the value of k is greater than or equal to 1;
generally speaking, a proper lambda value is selected through experiments, so that the lambda value is fixed, the same weighting factor is adopted for memorizing a target, a clutter and the clutter at the past moment, but an EMA algorithm for fixing a forgetting factor cannot have the advantages of being accurate in clutter measurement when the target, the clutter and the clutter at the past moment, and also being capable of quickly tracking when the target, the clutter and the clutter at the past moment, therefore, the EMA algorithm for fixing the forgetting factor is improved by adopting the forgetting factor lambda for adaptively adjusting the EMA algorithm, so that the effects of being accurate in clutter measurement when the target is stable and being capable of quickly tracking when the clutter is sudden change are achieved, and the method specifically comprises the following steps:
step S202: estimating the value of the clutter signal frame by a filter of an exponential moving average algorithm updating a forgetting factor λ of a kth frame
The following formula (1):
wherein k represents the kth frame, λ represents a forgetting factor, and λ is greater than or equal to 0 and less than or equal to 1;
step S30: according to the clutter signal
Iteratively calculating the suppressed output signal
The following formula (2) represents:
step S40: returning to the step S20, estimating clutter signals of a k +1 frame according to the output signals after the suppression of the k frame; and estimating clutter signals of a 2 nd frame through the echo signals of the 1 st frame, then calculating output signals of the 2 nd frame after suppression, then estimating clutter signals of a 3 rd frame through the output signals of the 2 nd frame, then calculating output signals … … of the 3 rd frame after suppression, and so on, and completing a cycle from the 1 st frame to the k th frame.
Specifically, dynamically adjusting the forgetting factor λ includes:
the step S201 of dynamically adjusting the weight parameter in the exponential moving average algorithm includes:
step S2011: according to the suppressed output signal
Performing a transformation and establishing a cost function, wherein the cost function is defined as the suppressed output signal
The square value of (c) is expressed by the following equation (3):
specifically, in step S2011, the cost function is the output signal
Such that when the amplitude of a certain distance unit of the frame abruptly changes, the cost function value increases abruptly; when the amplitude of a certain distance unit of the frame is kept stable, the cost function value approaches zero;
step S2012: and iteratively updating the forgetting factor lambda according to the function values of the cost function at different moments. (ii) a
Specifically, iteratively updating the forgetting factor λ includes:
step S2013: obtaining function values of the cost function at different moments, calculating a weighted sum of the cost function values at the past moment and the current moment, wherein the definition is used for calculating the weighted sum
For the weighted summation of the cost function values at the past time and the current time, the following formula (4) represents:
step S2014: according to
Iteratively updates the forgetting factor λ by the function value of (a), as expressed by the following equation (5):
wherein the content of the first and second substances,
indicates that the forgetting factor value is limited to [ lambda ] min ,λ max ]And the alpha and the beta are self-defined parameters.
The beneficial effects of the present invention in terms of computational complexity and clutter suppression performance are illustrated below by the synthetic data (actual measured clutter added to the simulated moving target).
(1) Aspect of computational complexity
The computational complexity is represented by the number of statistical additions and multiplications. Table 1 shows the calculation costs of the EMA algorithm with fixed parameters, the Yoo's algorithm, and the time-averaged forgetting factor algorithm (hereinafter, referred to as the algorithm of the present invention) used in the radar clutter suppression method of the present invention. Wherein N represents the iteration number of the algorithm, and M represents the distance unit number. As can be seen from table 1, compared with the Yoo's algorithm, the time-averaged forgetting factor-varying algorithm adopted in the present invention completes adaptive adjustment of the parameters of the EMA algorithm without increasing too much computational complexity.
TABLE 1 computational complexity analysis
| Algorithm | Number of times of addition | Number of multiplications |
| Fixed parameter EMA algorithm | 3NM | 2NM |
| Yoo's algorithm | 2NM 2 +3NM | 2NM 2 +4NM |
| Algorithm of the invention | 5NM | 6NM |
(2) Aspects of clutter suppression performance
In the simulation, an X4M03 radar is used for collecting clutter signals indoors, and sudden environmental changes are simulated by moving the radar at a certain moment. The radar main parameters are set as follows: f. of c ＝7.29GHz，f r ＝40.5MHz，FPS＝50Hz，R bin =0.0064m. On the basis of actually measured clutter signals, human body echoes are simulated and added into a simulated moving target, and then a simulation result is obtained through various clutter suppression algorithms.
The algorithm performance is mainly evaluated by the method of variation of the Improvement Factor (IF). It should be noted that the Improvement Factor (IF) is the ratio of the output signal-to-noise ratio to the input signal-to-noise ratio, and the specific formula (6) is as follows:
referring to fig. 2, the EMA algorithm with λ =0, the EMA algorithm with λ =0.7, the EMA algorithm with λ =0.9, the Yoo's algorithm, and the IF of the algorithm of the present invention are shown by different lines in fig. 2, and it can be seen from the enlarged view in the middle of fig. 2 that the convergence speed is λ =0, the algorithm of the present invention, the Yoo's algorithm, the EMA algorithm with λ =0.7, and the EMA algorithm with λ =0.9 in order from high to low; when the environment is stable, the height of the clutter suppression level is kept to be sequentially the EMA algorithm with the lambda =0.9, the algorithm of the invention, the EMA algorithm with the lambda =0.7, the Yoo's algorithm and the EMA algorithm with the lambda =0 from high to low, therefore, the algorithm of the invention keeps a higher clutter suppression level in the stable period of the environment, can be quickly converged after the environment is suddenly changed, well integrates the advantages of the large-parameter EMA algorithm and the small-parameter EMA algorithm, and achieves the purpose of self-adaptively suppressing time-varying clutter signals better and faster.
Referring to fig. 3-7, fig. 3-7 respectively show the signal amplitude diagrams of frame 1500 after no clutter suppression, EMA algorithm λ =0, EMA algorithm λ =0.7, yoo's algorithm, and algorithm of the present invention, and the clutter suppression situation of the sudden clutter environment is expressed by the signal amplitude diagrams of the respective algorithms. As can be seen from fig. 5, 6 and 7, when the clutter environment is suddenly changed, the algorithm of the present invention can complete clutter suppression more quickly and highlight the target than the EMA algorithm with λ =0.7 and the Yoo's algorithm.
Referring to fig. 8-12, fig. 8-12 respectively show the signal amplitude diagrams of the 1800 th frame after being processed by the algorithm of λ =0, the EMA algorithm of λ =0.7, the Yoo's algorithm and the algorithm of the present invention, and the stationary clutter suppression situation of the clutter environment is expressed by the signal amplitude diagrams of the respective algorithms, and it can be seen from fig. 8-12 that the algorithm of the present invention better protects the moving target signal in the stationary clutter environment than the EMA algorithm of λ =0.
In order to achieve the above object, the present invention further provides a radar clutter suppression apparatus, including a memory and a processor, wherein the memory stores a radar clutter suppression program operable on the processor, the radar clutter suppression program is based on a combined exponential moving average algorithm, and the radar clutter suppression program, when executed by the processor, implements the steps of the above radar clutter suppression method.
To achieve the above object, the present invention also provides a computer-readable storage medium having a radar clutter suppression program stored thereon, the radar clutter suppression program being executable by one or more processors to implement the steps of the radar clutter suppression method described above.
It should be noted that, the above numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another like element in a process, apparatus, article, or method that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (5)
1. A radar clutter suppression method is characterized by comprising the following steps: the method comprises the following steps:
step S10: acquiring an echo signal received by a radar;
wherein all reflected signals received by the radar are defined as echo signals
Wherein the content of the first and second substances,
the slow time dimension is represented as the kth frame, and the fast time dimension is represented as
A signal of a time of day;
step S20: estimating a clutter signal from the echo signal;
step S201: dynamically adjusting weight parameters in an exponential moving average algorithm, comprising: step S2011: according to the suppressed output signal
Performing a transformation and establishing a cost function, wherein the cost function is defined as the suppressed output signal
The square value of (c) is expressed by the following equation (3):
step S2012: obtaining function values of the cost function at different moments, calculating a weighted sum of the cost function values at the past moment and the current moment, wherein the definition is used for calculating the weighted sum
For the weighted summation of the cost function values at the past time and the current time, the following formula (4) represents:
step S2013: according to
Iteratively updates the forgetting factor λ by the function value of (a), as expressed by the following equation (5):
wherein the content of the first and second substances,
indicates that the forgetting factor value is limited to [ lambda ] min ,λ max ]In the formula, alpha and beta are self-defined parameters;
wherein the value of k is more than or equal to 1;
step S202: estimating the value of the clutter signal frame by a filter of an exponential moving average algorithm updating a forgetting factor λ of a kth frame
The following formula (1):
wherein k represents the kth frame, λ represents a forgetting factor, and λ is greater than or equal to 0 and less than or equal to 1;
step S30: according to the clutter signal
Iteratively calculating the suppressed output signal
The following formula (2) represents:
step S40: returning to step S20, the clutter signals of the k +1 frame are estimated from the suppressed output signal of the k-th frame.
2. The radar clutter suppression method of claim 1, wherein: the echo signal in step S10 includes a target signal, a clutter signal, and a noise signal.
3. The radar clutter suppression method of claim 1, wherein: in the step S2011, the cost function is the output signal
Such that when the amplitude of a certain distance unit of the kth frame abruptly changes, the cost function value increases abruptly; when the amplitude of a certain distance unit of the k-th frame keeps stable, the cost function value approaches zero.
4. A radar clutter suppression apparatus, characterized in that the apparatus comprises a memory and a processor, the memory having stored thereon a radar clutter suppression program operable on the processor, the radar clutter suppression program being based on a combined exponential moving average algorithm, the radar clutter suppression program, when executed by the processor, implementing the steps of the radar clutter suppression method according to any of claims 1 to 3.
5. A computer-readable storage medium having stored thereon a radar clutter suppression program executable by one or more processors to implement the steps of the radar clutter suppression method according to any of claims 1 to 3.
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