Method and device for determining antenna array of MIMO radar

Abstract

The invention discloses a method and a device for determining an antenna array of an MIMO radar, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first constraint condition for array element positions of a plurality of antenna array elements in an antenna array of the MIMO radar; obtaining a plurality of first position sequences meeting a first constraint condition and a first fitness function, and determining a plurality of second position sequences meeting the first constraint condition according to the plurality of first position sequences through a genetic algorithm according to an optimization target indicated by the first fitness function; obtaining a second constraint condition and a second fitness function; according to at least two optimization targets indicated by the second fitness function, determining a target position sequence meeting a second constraint condition according to the plurality of second position sequences through a genetic algorithm; and determining the plurality of antenna array elements arranged according to the target position sequence as a target antenna array of the MIMO radar. The technical problem of low design efficiency of the antenna array is solved.

Description

Technical Field

[0001] The present application relates to the field of radar, and in particular, to a method and device for determining an antenna array of a MIMO radar. Background Art

[0002] With the development of wireless communication and radar technology, MIMO radar systems have made significant progress in target detection, tracking and imaging. MIMO radar systems use multiple transmitting and receiving antennas to increase the system's freedom and flexibility. The design of antenna arrays is crucial to the performance of MIMO radar systems, but the performance of array antennas is a typical nonlinear problem. Therefore, many traditional antenna array designs are based on the designer's experience and are often limited by their experience and limitations. In addition, the design of radar antenna arrays based on experience is usually inefficient for different design requirements.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and device for determining an antenna array of a MIMO radar, so as to at least solve the technical problem of low efficiency in radar antenna design.

[0005] According to one aspect of an embodiment of the present application, a method for determining an antenna array of a MIMO radar is provided, comprising: obtaining a first constraint condition on array element positions of a plurality of antenna array elements in the antenna array of the MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength;

[0006] Acquire multiple first position sequences and a first fitness function that satisfy the first constraint, and determine multiple second position sequences that satisfy the first constraint through a genetic algorithm based on the multiple first position sequences according to the optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate the element position of each antenna element in the multiple antenna elements; acquire a second constraint and a second fitness function, wherein the second constraint indicates that the element interval between each two adjacent antenna elements is greater than half a wavelength, and the second fitness function is used to indicate at least two optimization targets; determine a target position sequence that satisfies the second constraint based on the multiple second position sequences through the genetic algorithm according to the at least two optimization targets indicated by the second fitness function; determine the multiple antenna elements arranged according to the target position sequence as the target antenna array of the MIMO radar.

[0007] According to another aspect of an embodiment of the present application, a device for determining an antenna array of a MIMO radar is also provided, including: a first acquisition unit, used to acquire a first constraint condition for array element positions of multiple antenna array elements in the antenna array of the MIMO radar, wherein the first constraint condition indicates that the array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength; a second acquisition unit, used to acquire multiple first position sequences and a first fitness function that satisfy the first constraint condition, and according to the optimization target indicated by the first fitness function, determine multiple second position sequences that satisfy the first constraint condition according to the multiple first position sequences through a genetic algorithm, wherein the first position sequences and the second position sequences are The position sequences are respectively used to indicate the array element positions of each antenna array element in the above-mentioned multiple antenna array elements; a third acquisition unit is used to obtain a second constraint condition and a second fitness function, wherein the above-mentioned second constraint condition indicates that the array element spacing between every two adjacent antenna array elements is greater than half a wavelength, and the above-mentioned second fitness function is used to indicate at least two optimization objectives; a first determination unit is used to determine a target position sequence that meets the above-mentioned second constraint condition according to the above-mentioned multiple second position sequences through the above-mentioned genetic algorithm according to the above-mentioned at least two optimization objectives indicated by the above-mentioned second fitness function; and a second determination unit is used to determine the above-mentioned multiple antenna array elements arranged according to the above-mentioned target position sequence as the target antenna array of the above-mentioned MIMO radar.

[0008] As an optional scheme, the above-mentioned first determination unit also includes: an optimization module, which is used to repeat the following steps until the number of updates to the N above-mentioned second position sequences is greater than or equal to the target threshold, wherein the above-mentioned N is an integer greater than 1: obtain the above-mentioned N second position sequences to be updated, and obtain the second fitness corresponding to each of the above-mentioned N second position sequences to be updated according to the above-mentioned second fitness function; according to the sorting results of the above-mentioned second fitness corresponding to each of the above-mentioned N second position sequences to be updated, obtain M above-mentioned second position sequences from the above-mentioned N above-mentioned second position sequences to be updated as the second position sequences to be processed, wherein the sorting of the above-mentioned second fitness corresponding to each of the above-mentioned M second position sequences is greater than or equal to the first order, and the above-mentioned M is an integer greater than 1 and less than N; perform genetic update processing on the above-mentioned M second position sequences to obtain M updated position sequences. , wherein each of the M updated position sequences satisfies the second constraint; the M updated position sequences and the remaining NM second position sequences in the N second position sequences are determined as the updated N second position sequences; when the number of updates corresponding to the updated N second position sequences is less than the target threshold, the updated N second position sequences are determined as the N second position sequences to be updated; when the number of updates corresponding to the updated N second position sequences is greater than or equal to the target threshold, the second fitness corresponding to each of the updated N second position sequences is obtained according to the second fitness function, and the second position sequence with the largest second fitness among the updated N second position sequences is determined as the target position sequence.

[0009] As an optional scheme, the above-mentioned optimization module is also used to: repeat the following steps until the N above-mentioned second position sequences to be updated are traversed: obtain the above-mentioned second position sequence from the above-mentioned N above-mentioned second position sequences to be updated as the current second position sequence; obtain the multiple function items included in the above-mentioned second fitness function, wherein the multiple above-mentioned function items respectively include an array evaluation index for indicating the above-mentioned optimization target; obtain the above-mentioned multiple array evaluation indicators of the above-mentioned antenna array arranged according to the current second position sequence, and determine the above-mentioned second fitness corresponding to the above-mentioned current second position sequence according to the multiple above-mentioned array evaluation indicators and the above-mentioned second fitness function.

[0010] As an optional scheme, the above-mentioned optimization module is also used to: obtain the maximum main-sidelobe ratio of the above-mentioned antenna array arranged according to the current second position sequence, determine the first coefficient according to the sum of the above-mentioned maximum main-sidelobe ratio and the first parameter, and determine the negative value of the power operation result with the above-mentioned first coefficient as the exponent and the target constant as the base as the first target value; obtain the array aperture of the above-mentioned antenna array arranged according to the current second position sequence, determine the second coefficient according to the sum of the above-mentioned array aperture and the first parameter, and determine the negative value of the power operation result with the above-mentioned second coefficient as the exponent and the above-mentioned target constant as the base as the second target value; determine the above-mentioned second fitness corresponding to the above-mentioned current second position sequence according to the above-mentioned first target value and the above-mentioned second target value.

[0011] As an optional solution, the above-mentioned optimization module is also used for: at least one of the following: obtaining two current second position sequences from the M above-mentioned second position sequences; obtaining a subsequence of the same length from each of the two above-mentioned current second position sequences; updating the two above-mentioned current second position sequences by exchanging the above-mentioned subsequences; obtaining a current second position sequence from the M above-mentioned second position sequences; updating the reference subsequence in the above-mentioned current second position sequence to obtain the above-mentioned updated position sequence, wherein the above-mentioned updated position sequence satisfies the above-mentioned second constraint condition.

[0012] As an optional solution, the above-mentioned optimization module is also used to: obtain the current update number corresponding to the N above-mentioned second position sequences to be updated; determine the mutation rate of the above-mentioned current second position sequence according to the above-mentioned current update number; determine the above-mentioned target length of the above-mentioned reference subsequence according to the product of the above-mentioned mutation rate and the sequence length of the above-mentioned current second position sequence.

[0013] As an optional scheme, the above-mentioned optimization module is also used for: when the number of updates corresponding to the above-mentioned N second position sequences to be updated meets the catastrophic condition, according to the ranking results of the above-mentioned second fitness corresponding to each of the above-mentioned N second position sequences to be updated, obtaining L of the above-mentioned second position sequences from the above-mentioned N second position sequences to be updated as the reserved second position sequences, wherein the ranking of the above-mentioned second fitness corresponding to each of the above-mentioned L second position sequences is greater than or equal to the second order, and the above-mentioned N is an integer greater than 1 and less than M; generating NL new position sequences according to the above-mentioned second constraint condition, and re-determining the NL new position sequences and the L second position sequences as the N second position sequences to be updated.

[0014] As an optional solution, the above-mentioned optimization module is also used to: perform genetic update processing on the M above-mentioned second position sequences to obtain M candidate position sequences; repeat the following steps until the M above-mentioned candidate position sequences are traversed: obtain one of the above-mentioned candidate position sequences from the M above-mentioned candidate position sequences as the current candidate position sequence; when the last array element position in the above-mentioned current candidate position sequence is greater than or equal to the constraint position, re-perform genetic update processing on the above-mentioned current candidate position sequence until the last array element position in the above-mentioned current candidate position sequence is less than the above-mentioned constraint position, and determine the above-mentioned current candidate position sequence as the above-mentioned updated position sequence; when the last array element position in the above-mentioned current candidate position sequence is less than the constraint position, determine the above-mentioned current candidate position sequence as the above-mentioned updated position sequence; wherein the above-mentioned constraint position is based on the difference between the maximum array aperture and N-1 times the above-mentioned half wavelength.

[0015] As an optional scheme, the above-mentioned optimization module is also used to: when the grating lobe position and the grating lobe width of the array pattern corresponding to the above-mentioned current candidate position sequence do not meet the first screening condition, re-genetically update the above-mentioned current candidate position sequence until the above-mentioned grating lobe position and the above-mentioned grating lobe width of the above-mentioned array pattern corresponding to the above-mentioned current candidate position sequence meet the above-mentioned first screening condition, and determine the above-mentioned current candidate position sequence as the above-mentioned updated position sequence.

[0016] As an optional scheme, the above-mentioned optimization module is also used to: obtain a candidate position subsequence from the above-mentioned current candidate position sequence; when the array index of the antenna array arranged according to the above-mentioned candidate position subsequence does not meet the second screening condition, re-genetically update the above-mentioned current candidate position sequence until the array index corresponding to the above-mentioned candidate position subsequence obtained according to the above-mentioned current candidate position sequence meets the above-mentioned second screening condition, and the above-mentioned current candidate position sequence is determined as the above-mentioned updated position sequence.

[0017] As an optional scheme, the above-mentioned second acquisition unit also includes: a pre-optimization module, which is used to repeat the following steps until the number of updates to the N above-mentioned first position sequences is greater than or equal to the reference threshold, wherein the above-mentioned N is an integer greater than 1: obtain the above-mentioned N first position sequences to be updated, and obtain the first fitness corresponding to each of the above-mentioned N first position sequences to be updated according to the above-mentioned first fitness function; according to the sorting results of the above-mentioned first fitness corresponding to each of the above-mentioned N first position sequences to be updated, obtain P above-mentioned first position sequences from the above-mentioned N first position sequences to be updated as the first position sequences to be processed, wherein the sorting of the above-mentioned first fitness corresponding to each of the above-mentioned P first position sequences is greater than or equal to the second order, and the above-mentioned P is an integer greater than 1 and less than N. ; Perform genetic update processing on the P first position sequences to obtain P updated position sequences, wherein each of the P updated position sequences satisfies the first constraint condition; determine the P updated position sequences and the remaining NP first position sequences in the N first position sequences as the updated N first position sequences; when the number of updates corresponding to the updated N first position sequences is less than the reference threshold, determine the updated N first position sequences as the N first position sequences to be updated; when the number of updates corresponding to the updated N first position sequences is greater than or equal to the reference threshold, determine the updated N first position sequences as the N second position sequences.

[0018] According to another aspect of the embodiment of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method for determining the antenna array of a MIMO radar.

[0019] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the antenna array of the MIMO radar through the computer program.

[0020] Through the above-mentioned embodiments of the present application, a first constraint condition on the array element position of multiple antenna array elements in the antenna array of the MIMO radar is obtained; multiple first position sequences satisfying the above-mentioned first constraint condition and a first fitness function are obtained, and according to the optimization target indicated by the above-mentioned first fitness function, multiple second position sequences satisfying the above-mentioned first constraint condition are determined through a genetic algorithm based on the multiple first position sequences; a second constraint condition and a second fitness function are obtained; according to the above-mentioned at least two optimization targets indicated by the above-mentioned second fitness function, a target position sequence satisfying the above-mentioned second constraint condition is determined through the above-mentioned genetic algorithm based on the multiple second position sequences; the multiple antenna array elements arranged according to the above-mentioned target position sequence are determined as the target antenna array of the above-mentioned MIMO radar, thereby efficiently determining the layout method of the target antenna array of the MIMO radar.

[0021] In the above implementation, since the first constraint condition is more stringent on the position relationship of the array elements, the corresponding solution space is smaller, and a better position sequence set can be quickly determined; then based on the better position sequence set, the constraint condition is further adjusted (i.e., adjusted to the second constraint condition), a new optimization goal is determined, the target position sequence is determined according to the genetic algorithm, and the array is designed based on the target position sequence, thereby improving the efficiency of searching for the optimal solution in the solution space and solving the technical problem of low efficiency of existing antenna array design. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 is a schematic diagram of an optional MIMO radar antenna array application environment according to an embodiment of the present application;

[0024] Figure 2 is a schematic diagram of a process of an optional method for determining an antenna array of a MIMO radar according to an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of an array arrangement method according to an embodiment of the present application;

[0026] Figure 4 is a fitting schematic diagram of an optimization process according to an embodiment of the present application;

[0027] Figure 5 is a schematic diagram of an individual crossover method according to an embodiment of the present application;

[0028] Figure 6is a schematic diagram of a process of another optional method for determining an antenna array of a MIMO radar according to an embodiment of the present application;

[0029] Figure 7 is a schematic diagram of a device for determining an antenna array of a MIMO radar according to an embodiment of the present application;

[0030] Figure 8 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] First, the technical terms that appear in this application are explained:

[0034] MIMO (Multiple-Input Multiple-Output) radar: MIMO radar uses multiple transmit and receive antennas to increase the freedom and flexibility of the system. The transmit antenna array and the receive antenna array of the MIMO radar antenna are separated from each other, and can perform signal processing as a whole system.

[0035] Maximum main-sidelobe ratio: the ratio of the peak intensity of the main lobe of the radar signal to the peak intensity of the strongest sidelobe;

[0036] Genetic algorithm: It is used to obtain better solutions based on a set of candidate solutions to the problem (called individuals, organisms or phenotypes). Each candidate solution has a set of characteristics that can mutate and change. In the process of obtaining better solutions based on candidate solutions, new solutions are generated based on better solutions by simulating the "mutation" and "crossover" that occur in the biological purification process.

[0037] According to one aspect of an embodiment of the present application, a method for determining an antenna array of a MIMO radar is provided. Optionally, the antenna array determined according to the above-mentioned method for determining an antenna array of a MIMO radar can be applied to, but is not limited to, Figure 1 In the hardware environment shown. Optionally, the MIMO radar antenna array determined by the above-mentioned method for determining the antenna array of the MIMO radar provided in the present application can be applied to a vehicle terminal. Figure 1 A side view of a vehicle terminal 101 is shown, and the vehicle terminal 101 can travel on a travel surface 113. The vehicle terminal 101 includes an onboard navigation system 103, a memory 102 storing a digitized road map 104, a space monitoring system 117, a vehicle controller 109, a GPS (Global Positioning System) sensor 110, an HMI (Human / Machine Interface) device 111, and also includes an autonomous controller 112 and a telematics controller 114.

[0038] In one embodiment, the space monitoring system 117 includes one or more space sensors and systems, which are used to monitor the visible area 105 in front of the vehicle terminal 101. The space monitoring system 117 also includes a space monitoring controller 118; the space sensors used to monitor the visible area 105 include a laser radar sensor 106, a radar sensor 107, a camera 108, etc. The space monitoring controller 118 can be used to generate data related to the visible area 105 based on the data input from the space sensor. The space monitoring controller 118 can determine the linear range, relative speed and trajectory of the vehicle terminal 101 based on the input from the space sensor, for example, determine the current speed of the vehicle and the relative speed compared to the front vehicle. The space sensor of the vehicle terminal space monitoring system 117 may include an object positioning sensing device, and the object positioning sensing device may include a range sensor, which can be used to locate the front object, such as the front vehicle object. The above-mentioned radar sensor 107 may include a target MIMO radar antenna array determined in the following manner:

[0039] S1, obtaining a first constraint condition on array element positions of a plurality of antenna array elements in an antenna array of a MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength;

[0040] S2, obtaining a plurality of first position sequences satisfying a first constraint condition and a first fitness function, and determining a plurality of second position sequences satisfying the first constraint condition through a genetic algorithm according to the plurality of first position sequences according to an optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna array element in the plurality of antenna array elements;

[0041] S3, obtaining a second constraint condition and a second fitness function, wherein the second constraint condition indicates that the array element spacing between every two adjacent antenna array elements is greater than half a wavelength, and the second fitness function is used to indicate at least two optimization objectives;

[0042] S4, determining a target position sequence satisfying a second constraint condition according to a plurality of second position sequences through a genetic algorithm according to at least two optimization objectives indicated by the second fitness function;

[0043] S5, determining the multiple antenna array elements arranged according to the target position sequence as the target antenna array of the MIMO radar.

[0044] The camera 108 is advantageously mounted and positioned on the vehicle terminal 101 in a position that allows capturing an image of the visible area 105, wherein at least a portion of the visible area 105 includes a portion of the travel surface 113 in front of the vehicle terminal 101 and including the trajectory of the vehicle terminal 101. The visible area 105 may also include the surrounding environment. Other cameras may also be used, for example, including a second camera disposed on the rear or side portion of the vehicle terminal 101 to monitor the rear of the vehicle terminal 101 and one of the right or left sides of the vehicle terminal 101.

[0045] The autonomous controller 112 is configured to implement autonomous driving or advanced driver assistance system (ADAS) vehicle terminal functionality. Such functionality may include a vehicle terminal onboard control system capable of providing a certain level of driving automation. Driving automation may include a range of dynamic driving and vehicle terminal operations. Driving automation may include a certain level of automatic control or intervention involving a single vehicle terminal function (e.g., steering, acceleration, and / or braking).

[0046] The HMI device 111 provides human-machine interaction for the purpose of guiding the operation of the infotainment system, GPS (Global Positioning System) sensor 110, onboard navigation system 103 and the like, and includes a controller. The HMI device 111 monitors operator requests and provides the operator with status, service and maintenance information of the vehicle terminal system. The HMI device 111 communicates with multiple operator interface devices and / or controls the operation of multiple operator interface devices. The HMI device 111 may also communicate with one or more devices that monitor biometric data associated with the vehicle terminal operator. For simplicity of description, the HMI device 111 is depicted as a single device, but in the embodiments of the system described herein, it may be configured as multiple controllers and associated sensing devices.

[0047] Operator controls may be included in the passenger compartment of the vehicle terminal 101, and may include, by way of non-limiting example, a steering wheel, an accelerator pedal, a brake pedal, and an operator input device, which is an element of the HMI device 111. The operator controls enable a vehicle terminal operator to interact with the operating vehicle terminal 101 and direct the operation of the vehicle terminal 101 to provide passenger transportation.

[0048] The onboard navigation system 103 uses the digitized road map 104 for the purpose of providing navigation support and information to the vehicle terminal operator. The autonomous controller 112 uses the digitized road map 104 for the purpose of controlling the autonomous vehicle terminal operation or ADAS vehicle terminal functions.

[0049] The vehicle terminal 101 may include a telematics controller 114, which includes a wireless telematics communication system capable of communicating outside the vehicle terminal (including communicating with a communication network 115 having wireless and wired communication capabilities). The wireless telematics communication system includes an off-board server 116 capable of short-range wireless communication with a mobile terminal.

[0050] Through the above-mentioned embodiments of the present application, a first constraint condition on the array element position of multiple antenna array elements in the antenna array of the MIMO radar is obtained; multiple first position sequences satisfying the above-mentioned first constraint condition and a first fitness function are obtained, and according to the optimization target indicated by the above-mentioned first fitness function, multiple second position sequences satisfying the above-mentioned first constraint condition are determined through a genetic algorithm based on the multiple first position sequences; a second constraint condition and a second fitness function are obtained; according to the above-mentioned at least two optimization targets indicated by the above-mentioned second fitness function, a target position sequence satisfying the above-mentioned second constraint condition is determined through the above-mentioned genetic algorithm based on the multiple second position sequences; the multiple antenna array elements arranged according to the above-mentioned target position sequence are determined as the target antenna array of the above-mentioned MIMO radar, thereby efficiently determining the layout method of the target antenna array of the MIMO radar.

[0051] In the above implementation, since the first constraint condition is more stringent on the position relationship of the array elements, the corresponding solution space is smaller, and a better position sequence set can be quickly determined; then based on the better position sequence set, the constraint condition is further adjusted (i.e., adjusted to the second constraint condition), a new optimization goal is determined, the target position sequence is determined according to the genetic algorithm, and the array is designed based on the target position sequence, thereby improving the efficiency of searching for the optimal solution in the solution space and solving the technical problem of low efficiency of existing antenna array design.

[0052] As an optional implementation, Figure 2 As shown, the method for determining the antenna array of the MIMO radar can be executed by an electronic device, and the antenna array of the MIMO radar determined based on the above-mentioned method for determining the antenna array of the MIMO radar can be applied to Figure 1 In the vehicle terminal shown, the method for determining the antenna array of the MIMO radar includes the following specific steps:

[0053] S202, obtaining a first constraint condition on array element positions of a plurality of antenna array elements in an antenna array of a MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength;

[0054] S204, obtaining a plurality of first position sequences satisfying a first constraint condition and a first fitness function, and determining a plurality of second position sequences satisfying the first constraint condition according to the plurality of first position sequences by using a genetic algorithm according to an optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna array element in the plurality of antenna array elements;

[0055] S206, obtaining a second constraint condition and a second fitness function, wherein the second constraint condition indicates that an array element interval between every two adjacent antenna array elements is greater than a half wavelength, and the second fitness function is used to indicate at least two optimization objectives;

[0056] S208, determining a target position sequence satisfying a second constraint condition according to a plurality of second position sequences through a genetic algorithm according to at least two optimization objectives indicated by the second fitness function;

[0057] S210: Determine a plurality of antenna array elements arranged according to a target position sequence as a target antenna array of a MIMO radar.

[0058] First, two types of antenna arrays involved in this embodiment, sparse array and sparse array, are described. Figure 3 As shown in the figure, a sparse array refers to removing some antenna elements from a uniform full array. The antenna array formed in this way has fewer elements at the same array aperture, and the spacing between adjacent elements is an integer multiple of half a wavelength; a sparse array refers to a certain number of elements being randomly arranged in the corresponding array aperture, and it is only necessary to meet the constrained element spacing requirements, and there is no need for the spacing between adjacent elements to be an integer multiple of half a wavelength.

[0059] It can be understood that the first constraint condition in the above steps S202 and S204 can be used to constrain the array element spacing between adjacent array elements in the array, so that the array element arrangement indicated by the multiple first position sequences obtained and the new first position sequence generated by the genetic algorithm satisfies the arrangement mode of the sparse array, and then a better first position sequence is searched in the solution space through the above genetic algorithm and the initial multiple first position sequences, and the multiple antenna arrays finally determined are determined as the second position sequence.

[0060] It should be noted that, in the process of determining multiple second position sequences that satisfy the first constraint condition based on multiple first position sequences through a genetic algorithm according to the optimization target indicated by the first fitness function, the multiple first position sequences can be regarded as the initial population in the above-mentioned genetic algorithm, and each first position sequence can be determined as an initial individual in the above-mentioned initial population, and the fitness of each individual is determined by the first fitness function, and then new individuals (i.e., new first position sequences) are generated using individuals with higher fitness, and then the above operation is repeated using the population including the new individuals to continuously generate new individuals based on individuals with high fitness, and finally, multiple second position sequences (i.e., multiple individuals in the final population) are determined based on the final population.

[0061] Then in the above step S206, the second constraint condition and the second fitness function are obtained, and then the above multiple second position sequences are used as new initial populations, and the target position sequence is generated by the genetic algorithm according to the optimization target indicated by the second fitness function. It can be understood that the above second constraint condition constrains the array element spacing between every two adjacent antenna array elements to be greater than half a wavelength, that is, the above constraint can be used to make the generated new position sequence meet the array element distribution characteristics of the sparse array. At the same time, the above second fitness function indicates multiple optimization targets, and then the optimal solution that meets the above multiple optimization targets is found in the solution space.

[0062] Through the above implementation of the present application, the first constraint condition for the array element position of multiple antenna array elements in the antenna array of the MIMO radar is obtained; multiple first position sequences and the first fitness function that satisfy the above first constraint condition are obtained, and according to the optimization target indicated by the above first fitness function, multiple second position sequences that satisfy the above first constraint condition are determined by the genetic algorithm according to the multiple first position sequences; the second constraint condition and the second fitness function are obtained; according to the above at least two optimization targets indicated by the above second fitness function, the target position sequence that satisfies the above second constraint condition is determined by the above genetic algorithm according to the multiple second position sequences; the multiple antenna array elements arranged according to the above target position sequence are determined as the target antenna array of the above MIMO radar. Thus, a better position sequence population is first determined based on the sparse array and the genetic algorithm, and further the target position sequence is determined according to the genetic algorithm with the sparse array as the optimization target, and the array design is performed based on the target position sequence, thereby improving the efficiency of searching for the optimal solution in the solution space and solving the technical problem of low efficiency of the existing antenna array design.

[0063] Through the above-mentioned embodiments of the present application, a first constraint condition on the array element position of multiple antenna array elements in the antenna array of the MIMO radar is obtained; multiple first position sequences satisfying the above-mentioned first constraint condition and a first fitness function are obtained, and according to the optimization target indicated by the above-mentioned first fitness function, multiple second position sequences satisfying the above-mentioned first constraint condition are determined through a genetic algorithm based on the multiple first position sequences; a second constraint condition and a second fitness function are obtained; according to the above-mentioned at least two optimization targets indicated by the above-mentioned second fitness function, a target position sequence satisfying the above-mentioned second constraint condition is determined through the above-mentioned genetic algorithm based on the multiple second position sequences; the multiple antenna array elements arranged according to the above-mentioned target position sequence are determined as the target antenna array of the above-mentioned MIMO radar, thereby efficiently determining the layout method of the target antenna array of the MIMO radar.

[0064] In the above implementation, since the constraints on the position relationship of the array elements of the sparse array are relatively strict, the corresponding solution space is smaller, and a better position sequence set can be quickly determined; based on the better position sequence set, the sparse array is further used as the optimization target, the target position sequence is determined according to the genetic algorithm, and the array design is performed based on the target position sequence, thereby improving the efficiency of searching for the optimal solution in the solution space and solving the technical problem of low efficiency of existing antenna array design.

[0065] In an optional embodiment, the above-mentioned obtaining multiple first position sequences satisfying the first constraint condition and the first fitness function, and determining multiple second position sequences satisfying the first constraint condition according to the multiple first position sequences by a genetic algorithm according to the optimization target indicated by the first fitness function includes:

[0066] Repeat the following steps until the number of updates to the N first position sequences is greater than or equal to the reference threshold, where N is an integer greater than 1:

[0067] S1, obtaining N first position sequences to be updated, and obtaining first fitness corresponding to each of the N first position sequences to be updated according to a first fitness function;

[0068] S2, according to the ranking results of the first fitnesses corresponding to the N first position sequences to be updated, obtaining P first position sequences from the N first position sequences to be updated as the first position sequences to be processed, wherein the ranking of the first fitnesses corresponding to the P first position sequences is greater than or equal to the second ranking, and P is an integer greater than 1 and less than N;

[0069] S3, performing genetic update processing on the P first position sequences to obtain P updated position sequences, wherein each of the P updated position sequences satisfies a first constraint condition;

[0070] S4, determining the P updated position sequences and the remaining NP first position sequences in the N first position sequences as updated N first position sequences;

[0071] S5-1, when the number of updates corresponding to the updated N first position sequences is less than a reference threshold, determining the updated N first position sequences as the N first position sequences to be updated;

[0072] S5-2: When the number of updates corresponding to the updated N first position sequences is greater than or equal to a reference threshold, the updated N first position sequences are determined as N second position sequences.

[0073] It can be understood that, in this embodiment, firstly, a genetic algorithm is used to optimize the first fitness function, and multiple second position sequences satisfying the first constraint are determined based on multiple first position sequences satisfying the first constraint. In other words, in the above embodiment of the present application, a genetic algorithm can be used to query a better solution in the solution space with the optimization target indicated by the first fitness function, and the new first position sequences generated by the genetic algorithm through genetic update processing all satisfy the above first constraint, that is, they are all position sequences used to characterize the sparse array.

[0074] It should be noted that the first fitness function can be used to indicate one of the following optimization objectives: maximum main-sidelobe ratio, virtual array aperture, radar angular resolution, main beam width, grating lobe position, grating lobe width, etc. In this embodiment, the optimization objective indicated by the first fitness function is not limited.

[0075] The above-mentioned second order is set as needed to determine the order of the first position sequence to be subjected to subsequent genetic operations. For example, after obtaining the first fitness corresponding to each first position sequence, each first position sequence is sorted based on the first fitness, and then the first position sequences ranked in the top 10 (10 is the second order) in the first fitness are determined as the first position sequences to be subjected to genetic update processing.

[0076] In addition, the above genetic update processing may include but is not limited to a processing method for generating new individuals (in this embodiment, it may specifically be a new first position sequence) in a genetic algorithm, for example, it may include but is not limited to at least one of the following: crossover processing, mutation processing, catastrophe processing, etc. In this embodiment, the specific type of the genetic update processing method is not limited.

[0077] The process of optimizing a sparse array by a genetic algorithm is described below in conjunction with a specific implementation.

[0078] Specifically, taking a sparse receiving array MIMO radar as an example and because the array design does not need to consider the interference of noise too much, it is assumed that there is no noise in an ideal state.

[0079] Assume that the transmitting array has M transmitting antennas and the array element spacing is d T , respectively transmit M mutually orthogonal signals s k (t); The receiving array has N receiving antennas, and the array element spacing is d R ,d R =Md T At an angle θ from the transmitting array, the synthetic signal of the target at a distance R is:

[0080]

[0081] In this embodiment, the assumed scenario of radar signal transmission is: the transmitting array sends the radar signal to a point R at an angle θ from the transmitting array, and the radar signal is reflected at the point, and the reflected signal is received by the receiving array. Therefore, in the above formula (1), τ r =2R / c, is the time required for the signal emitted by the radar array to reach the target. Φ is used to characterize the signal phase when the radar signal reaches the target, where Φ=(2πd T / λ)sinθ.

[0082] When the transmitted signal is reflected by the target at that location, the reflected signal received by the nth receiving antenna of the receiving array is as shown in the following formula (2):

[0083] x n (t) = p 1 (t-τ r / 2)e -j(n-1)MΦ (2)

[0084] If n receiving antennas are used as a receiving array, the reflected signal received by the entire receiving array is as shown in formula (3):

[0085]

[0086] Mathematically processing the above formula yields:

[0087]

[0088] The received signal y(t) is related to each transmitted orthogonal signal s k (t) Matched filtering, the M matching outputs are:

[0089]

[0090] Take k = 1, 2, ..., M, and the M outputs are equivalently transmitted beamformed at the receiving array:

[0091]

[0092] The first term in the above formula (6) is the receiving array pattern, and the second term is the equivalent transmitting array pattern, so the MIMO radar comprehensive receiving and transmitting pattern can be obtained from the above formula.

[0093] Considering that the selectivity of the position of the sparse array element is a fixed half-wavelength integer multiple point, under the given array aperture L, there are only 2L / λ positions where the element may exist. In this case, the presence of the element at these 2L / λ positions can be directly used as the feature of the individual in the population, with 1 representing the position with the element and 0 representing the position without the element. The generated element spacing satisfies the generated transmitting array and receiving array. According to the above formula (6), the comprehensive transmit and receive pattern of the MIMO radar is obtained, and the target value is optimized on the pattern. The optimization can be expressed as:

[0094]

[0095] Among them, min MSLL = p(t(x), r(y)) means that in the process of optimizing and solving through genetic algorithm, the optimization goal is to minimize the maximum main-sidelobe ratio (since the maximum main-sidelobe ratio is a negative value, it also means that the optimization goal is the maximum main-sidelobe ratio with the largest absolute value).

[0096] t(1)=1 indicates that there is an array element at the first position of the transmit array, and t(end)=1 indicates that there is an array element at the last position of the transmit array; r(1)=1 indicates that there is an array element at the first position of the transmit array, and r(end)=1 indicates that there is an array element at the last position of the transmit array; L tmin , L tmax , L rmin , L rmax They represent the minimum and maximum apertures of the transmitting array and the minimum and maximum apertures of the receiving array respectively.

[0097] It can be understood that in this embodiment, in the process of solving the sparse array by genetic algorithm, the corresponding first constraint condition can be the constraint on the position of the array element in the above formula (7) and the constraint that there may be array elements in 2L / λ positions.

[0098] Since the genetic algorithm solution of the sparse array is to improve the efficiency of the subsequent sparse array solution, the fitness function setting of the sparse array can be simplified, and only the (absolute value) maximum main-sidelobe ratio is used as the target. According to the array antenna pattern product theorem and the MIMO radar comprehensive transceiver pattern, the MIMO radar comprehensive transceiver directivity function is obtained as follows:

[0099]

[0100] In formula (8) Indicates the nth t The nth transmitting antenna and rThe position of the receiving antenna. For the convenience of the following expression, the above two variables are represented by d without special instructions. w represents whether there is an antenna at the position below the table, which is 1 if there is an antenna and 0 if there is no antenna. f is the directivity function of the array element. The first fitness function is:

[0101]

[0102] Where S represents the allowed sidelobe range of the pattern. The optimization model is: MSLL;

[0103] Through the above implementation of the present application, N first position sequences to be updated are obtained, and the first fitness corresponding to each of the N first position sequences to be updated is obtained according to the first fitness function; according to the sorting results of the first fitness corresponding to each of the N first position sequences to be updated, P first position sequences are obtained from the N first position sequences to be updated as the first position sequences to be processed, wherein the sorting of the first fitness corresponding to each of the P first position sequences is greater than or equal to the second order, and P is an integer greater than 1 and less than N; the P first position sequences are genetically updated to obtain P updated position sequences, wherein each of the P updated position sequences satisfies the first constraint condition; The P updated position sequences and the remaining NP first position sequences in the N first position sequences are determined as the updated N first position sequences; when the number of updates corresponding to the updated N first position sequences is less than a reference threshold, the updated N first position sequences are determined as the N first position sequences to be updated; when the number of updates corresponding to the updated N first position sequences is greater than or equal to the reference threshold, the updated N first position sequences are determined as N second position sequences, and then the maximum main-sidelobe ratio is used as the optimization target, and a suitable second position sequence (as the initial population of the sparse array) is determined through a genetic algorithm, thereby narrowing the solution space of the sparse array and improving the optimization efficiency of the sparse array.

[0104] In an optional implementation, the at least two optimization objectives indicated by the second fitness function, determining a target position sequence satisfying the second constraint condition according to a plurality of second position sequences through a genetic algorithm, comprises:

[0105] Repeat the following steps until the number of updates to the N second position sequences is greater than or equal to the target threshold, where N is an integer greater than 1:

[0106] S1, obtaining N second position sequences to be updated, and obtaining second fitness corresponding to each of the N second position sequences to be updated according to a second fitness function;

[0107] S2, according to the sorting results of the second fitnesses corresponding to the N second position sequences to be updated, obtaining M second position sequences from the N second position sequences to be updated as the second position sequences to be processed, wherein the sorting of the second fitnesses corresponding to the M second position sequences is greater than or equal to the first sorting, and M is an integer greater than 1 and less than N;

[0108] S3, performing genetic update processing on the M second position sequences to obtain M updated position sequences, wherein each of the M updated position sequences satisfies the second constraint condition;

[0109] S4, determining the M updated position sequences and the remaining NM second position sequences in the N second position sequences as updated N second position sequences;

[0110] S5-1, when the number of updates corresponding to the updated N second position sequences is less than the target threshold, determining the updated N second position sequences as the N second position sequences to be updated;

[0111] S5-2, when the number of updates corresponding to the N updated second position sequences is greater than or equal to the target threshold, the second fitness corresponding to each of the N updated second position sequences is obtained according to the second fitness function, and the second position sequence with the largest second fitness among the N updated second position sequences is determined as the target position sequence.

[0112] It should be noted that, in this embodiment, since the second constraint condition no longer imposes a constraint on the spacing between adjacent array elements satisfying a multiple of half a wavelength, the second constraint condition can be used to expand the solution space and further find an array element arrangement that satisfies a sparse array.

[0113] The above-mentioned second order is set as needed to determine the order of the second position sequence to be subjected to subsequent genetic operations. For example, after obtaining the second fitness corresponding to each second position sequence, each second position sequence is sorted based on the second fitness, and then the second position sequences ranked in the top 5 (5 is the first order) in the second fitness are determined as the second position sequences to be subjected to genetic update processing.

[0114] In an optional implementation, the above-mentioned obtaining the N second position sequences to be updated, and obtaining the second fitness corresponding to each of the N second position sequences according to the second fitness function includes:

[0115] Repeat the following steps until the N second position sequences to be updated are traversed:

[0116] S1, obtaining a second position sequence from the N second position sequences to be updated as a current second position sequence;

[0117] S2, obtaining a plurality of function items included in the second fitness function, wherein the plurality of function items respectively include an array evaluation index for indicating an optimization target;

[0118] S3, obtaining multiple array evaluation indicators of the antenna array arranged according to the current second position sequence, and determining the second fitness corresponding to the current second position sequence according to the multiple array evaluation indicators and the second fitness function.

[0119] It is understandable that, in this embodiment, the second fitness function can be used to indicate multiple optimization targets. Specifically, one optimization target can be indicated by each of the multiple function items included in the second fitness function. The multiple optimization targets may include but are not limited to at least one of the following: maximum main-sidelobe ratio, virtual array aperture, radar angular resolution, main beam width, grating lobe position, grating lobe width, etc. In this embodiment, the specific function items that may be included in the second fitness function and the corresponding optimization targets are not limited.

[0120] Optionally, each of the above function items may be configured with a corresponding weight coefficient to reflect the influence of each optimization objective on the optimal solution in the process of finding the optimal solution.

[0121] Optionally, each of the above-mentioned optimization objectives may also have different weights in different solution stages. For example, in the process of updating the population (generating a new second position sequence) through the above-mentioned genetic algorithm, the weight of the function item corresponding to the first optimization objective may be increased in the early stage (for example, the number of cycles is less than the first number); in the middle stage (for example, the number of cycles is greater than or equal to the first number, and less than the second number), the weight of the function item corresponding to the first optimization objective may be reduced, and the weight of the function item corresponding to the second optimization objective may be increased; in the final stage (for example, the number of cycles is greater than the second number, and less than the number of cycle ends), the weight of the function item corresponding to the first optimization objective may be adjusted to zero. Thus, the solution is performed according to different optimization objectives at different stages of the solution, providing a clear solution direction, thereby improving the solution efficiency.

[0122] In an optional implementation, the acquiring of multiple array evaluation indicators of the antenna array arranged according to the current second position sequence, and determining the second fitness corresponding to the current second position sequence according to the multiple array evaluation indicators and the second fitness function includes:

[0123] S1, obtaining a maximum main-sidelobe ratio of the antenna array arranged according to the current second position sequence, determining a first coefficient according to the sum of the maximum main-sidelobe ratio and the first parameter, and determining the negative value of a power operation result with the first coefficient as the exponent and the target constant as the base as the first target value;

[0124] S2, obtaining the array aperture of the antenna array arranged according to the current second position sequence, determining the second coefficient according to the sum of the array aperture and the first parameter, and determining the result of a power operation with the second coefficient as the exponent and the target constant as the base as the second target value;

[0125] S3, determining a second fitness corresponding to the current second position sequence according to the first target value and the second target value.

[0126] In a specific implementation, the sensitivity to different optimization targets during the search process can be adjusted by configuring the second fitness function. Specifically, the second fitness function can be set so that the genetic algorithm is more sensitive to the maximum main-sidelobe ratio during the search process, but will also be affected by the main beam width. After reaching a certain maximum main-sidelobe ratio, the search is performed more sensitively to the main beam width. If necessary, the performance can be replaced within the tolerance range of the maximum main-sidelobe ratio and the main beam width, while taking into account the maximum main-sidelobe ratio and the main beam width. The three-dimensional image of the fitting function is shown in FIG. Figure 4 As shown;

[0127] Specifically, the second fitness function can be expressed as follows:

[0128]

[0129] In the above formula (10), a, b, and c are related to the weights of the expected maximum main-sidelobe ratio and the array aperture (related to the main beam width). Specifically, a, b, and c represent the expected maximum sidelobe ratio, the expected minimum array aperture, and the conversion tolerance of the sidelobe ratio and the array aperture (meaning that we allow a sidelobe level of 0.1 dB in exchange for an increase in the array aperture of c half wavelengths).

[0130] Among them, -e SSL(i)+a When the maximum main-sidelobe ratio (negative value) corresponding to the position sequence generated in the optimization process reaches a, the search is performed more sensitively with the main beam width.

[0131] The optimization model is: MO;

[0132] In an optional implementation, the genetic update process is performed on the M second position sequences to obtain the M updated position sequences, which include at least one of the following:

[0133] Method 1: obtaining two current second position sequences from the M second position sequences; obtaining a subsequence of the same length from each of the two current second position sequences; and updating the two current second position sequences by exchanging the subsequences;

[0134] Method 2: obtaining a current second position sequence from the M second position sequences; updating the reference subsequence in the current second position sequence to obtain an updated position sequence, wherein the updated position sequence satisfies the second constraint condition.

[0135] It can be understood that the genetic update processing methods of the above-mentioned method 1 and method 2 correspond to the crossover processing and mutation processing in the genetic algorithm respectively, and the combination of the above-mentioned two methods can be determined according to the specific application scenario.

[0136] In an optional implementation, before the subsequence of the target length in the current second position sequence is updated to obtain the updated position sequence, the method further includes:

[0137] S1, obtaining the current update times corresponding to the N second position sequences to be updated;

[0138] S2, determining the mutation rate of the current second position sequence according to the current update number;

[0139] S3, determining a target length of the reference subsequence according to the product of the mutation rate and the sequence length of the current second position sequence.

[0140] It can be understood that in a specific implementation, in the initial stage of the algorithm, a large-scale incoherent matrix is ​​generated with a larger mutation rate to accelerate the algorithm to find the area where the optimal peak is located in multiple places, so as to improve the algorithm's ability to resist local optimality and search efficiency; in the final stage of the algorithm, a local search is performed with a smaller mutation rate in the hope that it can find the optimal solution in the local area as soon as possible, avoid invalid calculations caused by large-scale searches, and avoid convergence oscillations.

[0141] For the individual feature matrix F = [f 1 ,f 2 ,f 3 ,f 4 …,f M ] T (where f n Indicates a second position sequence respectively), in the early stage of search, the new individuals generated by the mutation operator with a larger mutation rate are: F′=[f 1 ,f 2 ′,f 3 ′,f 4 …,f M ] T ;

[0142] At the end of the search, the individual feature matrix F is also subjected to the mutation operator to generate a new individual feature matrix: F″=[f 1 ,f 2 ,f″ 3 ,f 4…,f M ] T ;

[0143] In a specific implementation, the above adaptive mutation rate can be expressed by the following formula (10):

[0144]

[0145] Among them, g represents the current number of iterations, and G represents the expected total number of iterations. The adaptive mutation rate indicated by the above formula (10) can make the genetic algorithm search for solutions. When the number of iterations is small compared to the total number of iterations, the mutation rate is large, which is convenient for finding the optimal solution as soon as possible. When the number of iterations is large compared to the total number of iterations, considering that the possibility of searching for the optimal solution becomes greater under the effect of the former, the mutation rate is reduced, which is convenient for better convergence of the results.

[0146] The following combination Figure 5 A specific crossover method is described below. According to the rules of genetic algorithm and the properties of antenna array, Figure 5 As shown, an improved crossover operator is introduced to select the individuals with the highest and second highest fitness for crossover, and the crossover rule is different from the normal crossover. The transmitting array and the receiving array are directly crossovered to generate part of the new population, in the hope that the new population can show better fitness according to the array correlation.

[0147] In an optional implementation, after obtaining the N second position sequences to be updated and obtaining the second fitness corresponding to each of the N second position sequences to be updated according to the second fitness function, the method further includes:

[0148] S1, when the update times corresponding to the N second position sequences to be updated meet the catastrophic condition, according to the sorting results of the second fitness corresponding to each of the N second position sequences to be updated, obtain L second position sequences from the N second position sequences to be updated as the reserved second position sequences, wherein the sorting of the second fitness corresponding to each of the L second position sequences is greater than or equal to the second order, and N is an integer greater than 1 and less than M;

[0149] S2, generating NL new position sequences according to the second constraint condition, and re-determining the NL new position sequences and the L second position sequences as N second position sequences to be updated.

[0150] It is understandable that the above-mentioned disaster conditions can be determined according to the number of iterative updates.

[0151] In the above implementation, in order to avoid searching for a local optimal solution, a catastrophe operator can be introduced to simulate natural disasters in the natural environment and perform a large-scale shuffle on the generated population at a certain point. However, in order to ensure the search efficiency, a certain proportion of high-quality individuals are retained.

[0152] In an optional implementation, the above-mentioned performing genetic update processing on the M second position sequences to obtain the M updated position sequences includes:

[0153] S1, performing genetic update processing on the M second position sequences to obtain M candidate position sequences;

[0154] S2, repeat the following steps until M candidate position sequences are traversed:

[0155] S2-1, obtaining a candidate position sequence from the M candidate position sequences as a current candidate position sequence;

[0156] S2-2, when the last array element position in the current candidate position sequence is greater than or equal to the constraint position, re-perform genetic update processing on the current candidate position sequence until the last array element position in the current candidate position sequence is less than the constraint position, and the current candidate position sequence is determined as the update position sequence;

[0157] S2-3, when the last array element position in the current candidate position sequence is less than the constrained position, the current candidate position sequence is determined as the updated position sequence;

[0158] The constraint position is based on the difference between the maximum array aperture and N-1 half wavelengths.

[0159] It can be understood that in the above-mentioned embodiments of the present application, during the genetic update process, each new position sequence generated can be constrained by the second constraint condition, and when the generated position sequence does not meet the constraint condition, a new position sequence is regenerated.

[0160] In a specific manner, the method for determining the second constraint condition can be shown by the following formula (11):

[0161]

[0162] Among them, x i Indicates the position of the i-th array element, L = L max -(N-1)λ / 2, it can be seen that the search space is L max Reduced L max -(N-1)λ / 2, and some spacing constraints are invisible and no longer need to be considered when generating the initial population, which reduces the workload of the algorithm.

[0163] In an optional embodiment, after obtaining a candidate position sequence from M candidate position sequences as the current candidate position sequence, the above-mentioned method also includes: when the grating lobe position and the grating lobe width of the array pattern corresponding to the current candidate position sequence do not meet the first screening condition, re-performing genetic update processing on the current candidate position sequence until the grating lobe position and the grating lobe width of the array pattern corresponding to the current candidate position sequence meet the first screening condition, and determining the current candidate position sequence as the updated position sequence.

[0164] In this embodiment, if the array pattern has grating lobes, when the radar measures angles, if part or all of the angular range of the grating lobes is also within the radar FOV, then multiple peaks will appear when the radar measures the angles, resulting in Ghost. Obviously, such an array does not meet the design requirements. The generated array can be further screened according to the grating lobe position and its width during the solution process.

[0165] In an optional implementation, after obtaining a candidate position sequence from the M candidate position sequences as the current candidate position sequence, the method further includes:

[0166] S1, obtain a candidate position subsequence from the current candidate position sequence;

[0167] S2, when the array index of the antenna array arranged according to the candidate position subsequence does not meet the second screening condition, the current candidate position sequence is genetically updated again until the array index corresponding to the candidate position subsequence obtained according to the current candidate position sequence meets the second screening condition, and the current candidate position sequence is determined as the updated position sequence.

[0168] It should be noted that in some scenarios, the radar selects different antenna elements to form different array arrangements to meet the needs of different working modes. Therefore, the second screening condition above allows the generated transceiver array to still have good performance when some elements are selected to form a new transceiver array. Therefore, after the array formed by all elements is optimized by the MOGA algorithm, the multiple arrays obtained are screened out by selecting some elements to test their array performance to select array designs that meet the requirements.

[0169] The following combination Figure 6 A complete process of this application is described.

[0170] First, receiving input parameters, which can be used to indicate the first constraint condition, the second constraint condition, the first fitness function, the second fitness function and other parameters used for genetic iteration. Then executing S602, generating a sparse matrix initial population;

[0171] It is understandable that the above-mentioned sparse array initial population can be multiple first position sequences, and multiple first position sequences all satisfy the first constraint condition. With respect to the first constraint condition, on the one hand, it is necessary to consider that the selectivity of the sparse array element position is a fixed half-wavelength integer multiple point. Under the given array aperture L, there are only 2L / λ positions where elements may exist. In this case, whether there are elements at these 2L / λ positions can be directly used as the characteristics of individuals in the population, with 1 representing the presence of an element at the position and 0 representing the absence of an element at the position. On the other hand, the element position can be constrained by the following formula (7):

[0172]

[0173] Next, execute S604, first judgment;

[0174] This judgment is used to determine whether the number of iterations meets the catastrophic condition. If the number of iterations meets the catastrophic condition, execute S604-1, catastrophic operator processing; otherwise, execute S604-2, fitness calculation.

[0175] In this embodiment, the above fitness calculation can be determined by the following formula (8) and formula (9):

[0176]

[0177]

[0178] Next, S606-S612 are executed in sequence: sorting operation; selection operation; crossover operation; mutation operation;

[0179] It can be understood that the above-mentioned sorting operation can sort each individual (position sequence) according to the result of the above-mentioned fitness calculation, and then perform a selection operation based on the sorting result, and determine the individual to perform the crossover operation and the mutation operation through the selection operation. In the process of generating a new individual through the mutation operation, the individual also needs to meet the first constraint condition. Then execute S614 to correct the population rule;

[0180] Next, execute S616, the second judgment; this step is to judge whether the end condition of solving the sparse matrix is ​​met by the number of iterations. If the end condition is not met, execute step S604;

[0181] When the end condition is met, execute S618, convert the sparse array into sparse array parameters;

[0182] Specifically, for sparse arrays, there is no fixed array element position for 0 and 1 removal operations, so it is necessary to consider using real number genetic algorithm for encoding, and directly represent the array element position by its distance from the first array element, for example: d T,3Represents the distance between the third transmitting antenna and the first transmitting antenna in the transmitting array. For such an array, according to the knowledge of antenna arrays, in order to obtain a higher main-sidelobe ratio without the appearance of grating lobes, it is necessary to make the array element spacing greater than half a wavelength, that is, d>λ / 2. Then the array element spacing matrix can be represented by the two matrices in formula (12).

[0183]

[0184] where d c =λ / 2,N T , N R Indicates the number of array elements in the transmitting array and the receiving array. In order to reduce the sparse array search space and thus improve the algorithm search efficiency, X can be directly used as the individual feature in the population to satisfy the conditions that the array element spacing should satisfy.

[0185] Then execute S620, the third judgment; this judgment is used to determine whether the number of iterations meets the disaster condition. If the number of iterations meets the disaster condition, execute S620-1, disaster operator processing; otherwise, execute S620-2, fitness calculation.

[0186] The second fitness function can be expressed as follows:

[0187]

[0188] In the above formula (10), a, b, and c are related to the weights of the expected maximum main-sidelobe ratio and the array aperture (related to the main beam width). Specifically, a, b, and c represent the expected maximum sidelobe ratio, the expected minimum array aperture, and the conversion tolerance of the sidelobe ratio and the array aperture (meaning that we allow a sidelobe level of 0.1 dB in exchange for an increase in the array aperture of c half wavelengths).

[0189] Among them, -e SSL(i)+a When the maximum main-sidelobe ratio (negative value) corresponding to the position sequence generated in the optimization process reaches a, the search is performed more sensitively with the main beam width.

[0190] The optimization model is: MO;

[0191] S622-S628 are executed in sequence, i.e., sorting operation; selection operation; crossover operation; mutation operation. It can be understood that the sorting operation can sort each individual (position sequence) according to the result of the fitness calculation, and then perform the selection operation based on the sorting result, and determine the individual to perform the crossover operation and mutation operation through the selection operation. In the process of generating a new individual through the mutation operation, the individual also needs to meet the second constraint condition.

[0192] In a specific implementation, at the initial stage of the algorithm, a large-scale incoherent matrix is ​​generated with a larger mutation rate to accelerate the algorithm to find the area where the optimal peak is located in multiple places, so as to improve the algorithm's ability to resist local optimality and search efficiency; at the end stage of the algorithm, a local search is performed with a smaller mutation rate in the hope that it can find the optimal solution in the local area as soon as possible, avoid invalid calculations caused by large-scale searches, and avoid convergence oscillations.

[0193] For the individual feature matrix F = [f 1 ,f 2 ,f 3 ,f 4 …,f M ] T (where f n Indicates a second position sequence respectively), in the early stage of the search, the new individuals generated by the mutation operator with a larger mutation rate are: F ′ =[f 1 ,f 2 ′ ,f 3 ′ ,f 4 …,f M ] T ;

[0194] At the end of the search, the individual feature matrix F is also subjected to the mutation operator to generate a new individual feature matrix: F″=[f 1 ,f 2 ,f 3 ″,f 4 …,f M ] T ;

[0195] In a specific implementation, the above adaptive mutation rate can be expressed by the following formula (10):

[0196]

[0197] Among them, g represents the current number of iterations, and G represents the expected total number of iterations. The adaptive mutation rate indicated by the above formula (10) can make the genetic algorithm search for solutions. When the number of iterations is small compared to the total number of iterations, the mutation rate is large, which is convenient for finding the optimal solution as soon as possible. When the number of iterations is large compared to the total number of iterations, considering that the possibility of searching for the optimal solution becomes greater under the effect of the former, the mutation rate is reduced, which is convenient for better convergence of the results.

[0198] Then execute S630 to correct the population rule; finally execute S632, the fourth judgment; that is, judge whether the end condition is met by the number of iterations, if the end condition is met, output the array, otherwise return to step S620.

[0199] Through the above implementation of the present application, the method is based on an improved genetic algorithm, and combines sparse arrays with sparse arrays, so that the search space is smaller and the search efficiency is significantly improved. And with the physical minimum spacing as a constraint, it brings greater population diversity, better plays the advantages of sparse arrays, and combines genetic algorithms and sparse arrays well. The integration of the transmitting array and the receiving array is completed by using MIMO technology. In the MIMO radar integrated array, multiple target values ​​are used as fitness, including the maximum main-sidelobe ratio that affects the radar receiving signal-to-noise ratio and the virtual array aperture that affects the radar angular resolution. The fitting function is used to select and search between the maximum main-sidelobe ratio and the array virtual aperture according to a certain trade-off. From the experimental results, it is observed that the proposed method effectively improves the search efficiency by combining sparse and sparse arrays, and achieves the expected goal by selecting multiple target fitness functions, providing a reliable method for MIMO radar array design.

[0200] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0201] According to another aspect of the embodiment of the present application, a device for determining the antenna array of a MIMO radar for implementing the above-mentioned method for determining the antenna array of a MIMO radar is also provided. Figure 7 As shown, the device comprises:

[0202] A first acquisition unit 702 is used to acquire a first constraint condition on the array element positions of a plurality of antenna array elements in the antenna array of the MIMO radar, wherein the first constraint condition indicates that the array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength;

[0203] A second acquisition unit 704 is used to acquire a plurality of first position sequences satisfying the first constraint condition and a first fitness function, and determine a plurality of second position sequences satisfying the first constraint condition according to the plurality of first position sequences by a genetic algorithm according to the optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna element in the plurality of antenna elements;

[0204] A third acquisition unit 706 is used to acquire a second constraint condition and a second fitness function, wherein the second constraint condition indicates that the array element interval between each two adjacent antenna array elements is greater than a half wavelength, and the second fitness function is used to indicate at least two optimization objectives;

[0205] A first determining unit 708 is configured to determine a target position sequence satisfying the second constraint condition according to the plurality of second position sequences through the genetic algorithm according to the at least two optimization objectives indicated by the second fitness function;

[0206] The second determining unit 710 is used to determine the plurality of antenna array elements arranged according to the target position sequence as the target antenna array of the MIMO radar.

[0207] As an optional solution, the first determination unit 702 further includes: an optimization module, which is used to repeat the following steps until the number of updates to the N second position sequences is greater than or equal to the target threshold, wherein N is an integer greater than 1: obtaining the N second position sequences to be updated, and obtaining the second fitness corresponding to each of the N second position sequences to be updated according to the second fitness function; obtaining M second position sequences from the N second position sequences to be updated as second position sequences to be processed according to the ranking results of the second fitness corresponding to each of the N second position sequences to be updated, wherein the ranking of the second fitness corresponding to each of the M second position sequences is greater than or equal to the first order, and M is an integer greater than 1 and less than N; performing genetic update processing on the M second position sequences to obtain M updated position sequences. sequence, wherein each of the M updated position sequences satisfies the second constraint; the M updated position sequences and the remaining NM second position sequences in the N second position sequences are determined as the updated N second position sequences; when the number of updates corresponding to the updated N second position sequences is less than the target threshold, the updated N second position sequences are determined as the N second position sequences to be updated; when the number of updates corresponding to the updated N second position sequences is greater than or equal to the target threshold, the second fitness corresponding to each of the updated N second position sequences is obtained according to the second fitness function, and the second position sequence with the largest second fitness among the updated N second position sequences is determined as the target position sequence.

[0208] As an optional scheme, the above-mentioned optimization module is also used to: repeat the following steps until the N above-mentioned second position sequences to be updated are traversed: obtain the above-mentioned second position sequence from the above-mentioned N above-mentioned second position sequences to be updated as the current second position sequence; obtain the multiple function items included in the above-mentioned second fitness function, wherein the multiple above-mentioned function items respectively include an array evaluation index for indicating the above-mentioned optimization target; obtain the above-mentioned multiple array evaluation indicators of the above-mentioned antenna array arranged according to the current second position sequence, and determine the above-mentioned second fitness corresponding to the above-mentioned current second position sequence according to the multiple above-mentioned array evaluation indicators and the above-mentioned second fitness function.

[0209] As an optional scheme, the above-mentioned optimization module is also used to: obtain the main-sidelobe ratio of the above-mentioned antenna array arranged according to the current second position sequence, determine the first coefficient according to the sum of the above-mentioned main-sidelobe ratio and the first parameter, and determine the negative value of the power operation result with the above-mentioned first coefficient as the exponent and the target constant as the base as the first target value; obtain the array aperture of the above-mentioned antenna array arranged according to the current second position sequence, determine the second coefficient according to the sum of the above-mentioned array aperture and the first parameter, and determine the negative value of the power operation result with the above-mentioned second coefficient as the exponent and the above-mentioned target constant as the base as the second target value; determine the above-mentioned second fitness corresponding to the above-mentioned current second position sequence according to the above-mentioned first target value and the above-mentioned second target value.

[0210] As an optional solution, the above-mentioned optimization module is also used for: at least one of the following: obtaining two current second position sequences from the M above-mentioned second position sequences; obtaining a subsequence of the same length from each of the two above-mentioned current second position sequences; updating the two above-mentioned current second position sequences by exchanging the above-mentioned subsequences; obtaining a current second position sequence from the M above-mentioned second position sequences; updating the reference subsequence in the above-mentioned current second position sequence to obtain the above-mentioned updated position sequence, wherein the above-mentioned updated position sequence satisfies the above-mentioned second constraint condition.

[0211] As an optional solution, the above-mentioned optimization module is also used to: obtain the current update number corresponding to the N above-mentioned second position sequences to be updated; determine the mutation rate of the above-mentioned current second position sequence according to the above-mentioned current update number; determine the above-mentioned target length of the above-mentioned reference subsequence according to the product of the above-mentioned mutation rate and the sequence length of the above-mentioned current second position sequence.

[0212] As an optional scheme, the above-mentioned optimization module is also used for: when the number of updates corresponding to the above-mentioned N second position sequences to be updated meets the catastrophic condition, according to the ranking results of the above-mentioned second fitness corresponding to each of the above-mentioned N second position sequences to be updated, obtaining L of the above-mentioned second position sequences from the above-mentioned N second position sequences to be updated as the reserved second position sequences, wherein the ranking of the above-mentioned second fitness corresponding to each of the above-mentioned L second position sequences is greater than or equal to the second order, and the above-mentioned N is an integer greater than 1 and less than M; generating NL new position sequences according to the above-mentioned second constraint condition, and re-determining the NL new position sequences and the L second position sequences as the N second position sequences to be updated.

[0213] As an optional solution, the above-mentioned optimization module is also used to: perform genetic update processing on the M above-mentioned second position sequences to obtain M candidate position sequences; repeat the following steps until the M above-mentioned candidate position sequences are traversed: obtain one of the above-mentioned candidate position sequences from the M above-mentioned candidate position sequences as the current candidate position sequence; when the last array element position in the above-mentioned current candidate position sequence is greater than or equal to the constraint position, re-perform genetic update processing on the above-mentioned current candidate position sequence until the last array element position in the above-mentioned current candidate position sequence is less than the above-mentioned constraint position, and determine the above-mentioned current candidate position sequence as the above-mentioned updated position sequence; when the last array element position in the above-mentioned current candidate position sequence is less than the constraint position, determine the above-mentioned current candidate position sequence as the above-mentioned updated position sequence; wherein the above-mentioned constraint position is based on the difference between the maximum array aperture and N-1 times the above-mentioned half wavelength.

[0214] As an optional scheme, the above-mentioned optimization module is also used to: when the grating lobe position and the grating lobe width of the array pattern corresponding to the above-mentioned current candidate position sequence do not meet the first screening condition, re-genetically update the above-mentioned current candidate position sequence until the above-mentioned grating lobe position and the above-mentioned grating lobe width of the above-mentioned array pattern corresponding to the above-mentioned current candidate position sequence meet the above-mentioned first screening condition, and determine the above-mentioned current candidate position sequence as the above-mentioned updated position sequence.

[0215] As an optional scheme, the above-mentioned optimization module is also used to: obtain a candidate position subsequence from the above-mentioned current candidate position sequence; when the array index of the antenna array arranged according to the above-mentioned candidate position subsequence does not meet the second screening condition, re-genetically update the above-mentioned current candidate position sequence until the array index corresponding to the above-mentioned candidate position subsequence obtained according to the above-mentioned current candidate position sequence meets the above-mentioned second screening condition, and the above-mentioned current candidate position sequence is determined as the above-mentioned updated position sequence.

[0216] As an optional solution, the second acquisition unit 704 further includes: a pre-optimization module, which is used to repeat the following steps until the number of updates to the N first position sequences is greater than or equal to a reference threshold, wherein N is an integer greater than 1: obtain the N first position sequences to be updated, and obtain the first fitness corresponding to each of the N first position sequences to be updated according to the first fitness function; according to the sorting results of the first fitness corresponding to each of the N first position sequences to be updated, obtain P first position sequences from the N first position sequences to be updated as the first position sequences to be processed, wherein the sorting of the first fitness corresponding to each of the P first position sequences is greater than or equal to the second sorting, and P is an integer greater than 1 and less than N. integer; performing genetic update processing on the P first position sequences to obtain P updated position sequences, wherein each of the P updated position sequences satisfies the first constraint; determining the P updated position sequences and the remaining NP first position sequences in the N first position sequences as the updated N first position sequences; when the number of updates corresponding to the updated N first position sequences is less than the reference threshold, determining the updated N first position sequences as the N first position sequences to be updated; when the number of updates corresponding to the updated N first position sequences is greater than or equal to the reference threshold, determining the updated N first position sequences as the N second position sequences.

[0217] For a specific embodiment, reference may be made to the example shown in the method for determining the antenna array of the MIMO radar described above, which will not be described in detail in this example.

[0218] According to another aspect of the embodiment of the present invention, an electronic device for implementing the above-mentioned method for determining the antenna array of the MIMO radar is also provided. The electronic device may be Figure 8 The terminal device or server shown in the figure. This embodiment is described by taking the electronic device as a terminal device as an example. Figure 8 As shown, the electronic device includes a memory 802 and a processor 804. The memory 802 stores a computer program, and the processor 804 is configured to execute the steps in any of the above method embodiments through the computer program.

[0219] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0220] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0221] S1, obtaining a first constraint condition on array element positions of a plurality of antenna array elements in an antenna array of a MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength;

[0222] S2, obtaining a plurality of first position sequences satisfying the first constraint condition and a first fitness function, and determining a plurality of second position sequences satisfying the first constraint condition according to the plurality of first position sequences through a genetic algorithm according to the optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna array element in the plurality of antenna array elements;

[0223] S3, obtaining a second constraint condition and a second fitness function, wherein the second constraint condition indicates that the array element spacing between every two adjacent antenna array elements is greater than half a wavelength, and the second fitness function is used to indicate at least two optimization objectives;

[0224] S4, according to the at least two optimization objectives indicated by the second fitness function, determining a target position sequence satisfying the second constraint condition according to the plurality of second position sequences through the genetic algorithm;

[0225] S5, determining the plurality of antenna array elements arranged according to the target position sequence as the target antenna array of the MIMO radar.

[0226] Alternatively, a person skilled in the art may understand that: Figure 8 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID), a PAD, and other terminal devices. Figure 8 The electronic device and the electronic equipment described above are not limited in structure. Figure 8 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 8 Different configurations shown.

[0227] Among them, the memory 802 can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the antenna array of the MIMO radar in the embodiment of the present invention. The processor 804 executes various functional applications and data processing by running the software programs and modules stored in the memory 802, that is, the method for determining the antenna array of the MIMO radar mentioned above is realized. The memory 802 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 802 may further include a memory remotely arranged relative to the processor 804, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 802 may be specifically, but not limited to, used to store information such as various elements in the scene picture, determination information of the antenna array of the MIMO radar, and so on. As an example, such as Figure 8 As shown, the memory 802 may include, but is not limited to, the first acquisition unit 702, the second acquisition unit 704, the third acquisition unit 706, the first determination unit 708, and the second determination unit 710 in the device for determining the antenna array of the MIMO radar. In addition, other module units in the device for determining the antenna array of the MIMO radar may also be included but are not limited to, which will not be repeated in this example.

[0228] Optionally, the transmission device 806 is used to receive or send data via a network. Specific examples of the network may include wired networks and wireless networks. In one example, the transmission device 806 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 806 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0229] In addition, the electronic device further includes: a display 808 for displaying a virtual scene in an interface; and a connection bus 810 for connecting various module components in the electronic device.

[0230] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. Among them, the nodes may form a peer-to-peer (P2P, Peer To Peer) network, and any form of computing device, such as a server, terminal or other electronic device, may become a node in the blockchain system by joining the peer-to-peer network.

[0231] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0232] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0233] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned method for determining the antenna array of the MIMO radar.

[0234] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0235] S1, obtaining a first constraint condition on array element positions of a plurality of antenna array elements in an antenna array of a MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength;

[0236] S2, obtaining a plurality of first position sequences satisfying the first constraint condition and a first fitness function, and determining a plurality of second position sequences satisfying the first constraint condition according to the plurality of first position sequences through a genetic algorithm according to the optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna array element in the plurality of antenna array elements;

[0237] S3, obtaining a second constraint condition and a second fitness function, wherein the second constraint condition indicates that the array element spacing between every two adjacent antenna array elements is greater than half a wavelength, and the second fitness function is used to indicate at least two optimization objectives;

[0238] S4, according to the at least two optimization objectives indicated by the second fitness function, determining a target position sequence satisfying the second constraint condition according to the plurality of second position sequences through the genetic algorithm;

[0239] S5, determining the plurality of antenna array elements arranged according to the target position sequence as the target antenna array of the MIMO radar.

[0240] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0241] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to perform all or part of the steps of the above methods of various embodiments of the present invention.

[0242] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0243] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0244] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0245] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0246] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining an antenna array of a MIMO radar, characterized in that: include: Acquire a first constraint condition on array element positions of a plurality of antenna array elements in an antenna array of a MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength; Acquire a plurality of first position sequences and a first fitness function that satisfy the first constraint condition, and determine a plurality of second position sequences that satisfy the first constraint condition according to the plurality of first position sequences by a genetic algorithm according to the optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna array element in the plurality of antenna array elements; Acquire a second constraint condition and a second fitness function, wherein the second constraint condition indicates that the array element spacing between every two adjacent antenna array elements is greater than half a wavelength, and the second fitness function is used to indicate at least two optimization objectives; According to the at least two optimization objectives indicated by the second fitness function, determining a target position sequence satisfying the second constraint condition according to a plurality of second position sequences by using the genetic algorithm; The plurality of antenna array elements arranged according to the target position sequence are determined as the target antenna array of the MIMO radar.

2. The method according to claim 1, characterized in that The at least two optimization objectives indicated by the second fitness function, determining a target position sequence satisfying the second constraint condition according to a plurality of second position sequences by the genetic algorithm, comprises: Repeat the following steps until the number of updates to the N second position sequences is greater than or equal to the target threshold, where N is an integer greater than 1: Acquire N second position sequences to be updated, and acquire second fitness corresponding to each of the N second position sequences to be updated according to the second fitness function; According to the ranking results of the second fitnesses respectively corresponding to the N second position sequences to be updated, M second position sequences are obtained from the N second position sequences to be updated as second position sequences to be processed, wherein the ranking of the second fitnesses respectively corresponding to the M second position sequences is greater than or equal to the first ranking, and M is an integer greater than 1 and less than N; Performing genetic update processing on the M second position sequences to obtain M updated position sequences, wherein each of the M updated position sequences satisfies the second constraint condition; Determine the remaining NM second position sequences among the M updated position sequences and the N second position sequences as the updated N second position sequences; When the number of updates corresponding to the updated N second position sequences is less than the target threshold, determining the updated N second position sequences as the N second position sequences to be updated; When the number of updates corresponding to the N updated second position sequences is greater than or equal to the target threshold, the second fitness corresponding to each of the N updated second position sequences is obtained according to the second fitness function, and the second position sequence with the largest second fitness among the N updated second position sequences is determined as the target position sequence.

3. The method according to claim 2, characterized in that The obtaining of the N second position sequences to be updated, and obtaining the second fitness corresponding to each of the N second position sequences according to the second fitness function includes: Repeat the following steps until the N second position sequences to be updated are traversed: Acquire one second position sequence from the N second position sequences to be updated as a current second position sequence; Acquire a plurality of function items included in the second fitness function, wherein each of the plurality of function items includes an array evaluation index for indicating the optimization target; Acquire a plurality of array evaluation indicators of the antenna array arranged according to the current second position sequence, and determine the second fitness corresponding to the current second position sequence according to the plurality of array evaluation indicators and the second fitness function.

4. The method according to claim 3, characterized in that The acquiring a plurality of array evaluation indicators of the antenna array arranged according to the current second position sequence, and determining the second fitness corresponding to the current second position sequence according to the plurality of array evaluation indicators and the second fitness function comprises: Obtain a maximum main-sidelobe ratio of the antenna array arranged according to the current second position sequence, determine a first coefficient according to the sum of the maximum main-sidelobe ratio and a first parameter, and determine the negative value of a power operation result with the first coefficient as an exponent and a target constant as a base as a first target value; Acquire an array aperture of the antenna array arranged according to the current second position sequence, determine a second coefficient according to the sum of the array aperture and the first parameter, and determine a power operation result with the second coefficient as an exponent and the target constant as a base as a second target value; The second fitness corresponding to the current second position sequence is determined according to the first target value and the second target value.

5. The method according to claim 2, characterized in that: The genetic updating process is performed on the M second position sequences to obtain the M updated position sequences, which includes at least one of the following: Obtain two current second position sequences from the M second position sequences; obtain a subsequence of the same length from each of the two current second position sequences; and update the two current second position sequences by exchanging the subsequences; A current second position sequence is obtained from the M second position sequences; a reference subsequence in the current second position sequence is updated to obtain the updated position sequence, wherein the updated position sequence satisfies the second constraint condition.

6. The method according to claim 5, characterized in that Before the subsequence of the target length in the current second position sequence is updated to obtain the updated position sequence, the method further includes: Obtaining the current update times corresponding to the N second position sequences to be updated; Determine the mutation rate of the current second position sequence according to the current update number; The target length of the reference subsequence is determined according to the product of the mutation rate and the sequence length of the current second position sequence.

7. The method according to claim 2, characterized in that: After obtaining the N second position sequences to be updated and obtaining the second fitness corresponding to each of the N second position sequences to be updated according to the second fitness function, the method further includes: When the update times corresponding to the N second position sequences to be updated meet the catastrophic condition, according to the ranking results of the second fitness corresponding to each of the N second position sequences to be updated, L second position sequences are obtained from the N second position sequences to be updated as the reserved second position sequences, wherein the ranking of the second fitness corresponding to each of the L second position sequences is greater than or equal to the second order, and N is an integer greater than 1 and less than M; NL new position sequences are generated according to the second constraint condition, and the NL new position sequences and the L second position sequences are re-determined as N second position sequences to be updated.

8. The method according to claim 2, characterized in that: The performing genetic update processing on the M second position sequences to obtain M updated position sequences comprises: Performing genetic update processing on the M second position sequences to obtain M candidate position sequences; Repeat the following steps until M candidate position sequences are traversed: Acquire one of the candidate position sequences from the M candidate position sequences as a current candidate position sequence; In the case where the last array element position in the current candidate position sequence is greater than or equal to the constraint position, re-performing genetic update processing on the current candidate position sequence until the last array element position in the current candidate position sequence is less than the constraint position, and determining the current candidate position sequence as the updated position sequence; In a case where the last array element position in the current candidate position sequence is smaller than the constrained position, determining the current candidate position sequence as the updated position sequence; The constrained position is based on the difference between the maximum array aperture and N-1 times the half wavelength.

9. The method according to claim 8, characterized in that After obtaining one of the candidate position sequences from the M candidate position sequences as the current candidate position sequence, the method further includes: When the grating lobe position and the grating lobe width of the array pattern corresponding to the current candidate position sequence do not satisfy the first screening condition, the current candidate position sequence is genetically updated again until the grating lobe position and the grating lobe width of the array pattern corresponding to the current candidate position sequence satisfy the first screening condition, and the current candidate position sequence is determined as the updated position sequence.

10. The method according to claim 8, characterized in that After obtaining one of the candidate position sequences from the M candidate position sequences as the current candidate position sequence, the method further includes: Acquire a candidate position subsequence from the current candidate position sequence; When the array index of the antenna array arranged according to the candidate position subsequence does not meet the second screening condition, the current candidate position sequence is genetically updated again until the array index corresponding to the candidate position subsequence obtained according to the current candidate position sequence meets the second screening condition, and the current candidate position sequence is determined as the updated position sequence.

11. The method according to claim 1, characterized in that: The step of acquiring a plurality of first position sequences satisfying the first constraint condition and a first fitness function, and determining a plurality of second position sequences satisfying the first constraint condition according to the plurality of first position sequences by a genetic algorithm according to the optimization target indicated by the first fitness function comprises: Repeat the following steps until the number of updates to the N first position sequences is greater than or equal to a reference threshold, where N is an integer greater than 1: Acquire N first position sequences to be updated, and acquire first fitness corresponding to each of the N first position sequences to be updated according to the first fitness function; According to the ranking results of the first fitnesses respectively corresponding to the N first position sequences to be updated, P first position sequences are obtained from the N first position sequences to be updated as first position sequences to be processed, wherein the ranking of the first fitnesses respectively corresponding to the P first position sequences is greater than or equal to the second ranking, and P is an integer greater than 1 and less than N; Performing genetic update processing on the P first position sequences to obtain P updated position sequences, wherein each of the P updated position sequences satisfies the first constraint condition; Determine the P updated position sequences and the remaining NP first position sequences in the N first position sequences as the updated N first position sequences; When the number of updates corresponding to the updated N first position sequences is less than the reference threshold, determining the updated N first position sequences as the N first position sequences to be updated; When the number of updates corresponding to the updated N first position sequences is greater than or equal to the reference threshold, the updated N first position sequences are determined as N second position sequences.

12. A device for determining an antenna array of a MIMO radar, characterized in that: include: A first acquisition unit is used to acquire a first constraint condition on array element positions of a plurality of antenna array elements in an antenna array of a MIMO radar, wherein the first constraint condition indicates that an array element spacing between every two adjacent antenna array elements is an integer multiple of a half wavelength; a second acquisition unit, configured to acquire a plurality of first position sequences satisfying the first constraint condition and a first fitness function, and determine a plurality of second position sequences satisfying the first constraint condition according to the plurality of first position sequences by a genetic algorithm according to the optimization target indicated by the first fitness function, wherein the first position sequence and the second position sequence are respectively used to indicate an element position of each antenna array element in the plurality of antenna array elements; A third acquisition unit, configured to acquire a second constraint condition and a second fitness function, wherein the second constraint condition indicates that the array element spacing between every two adjacent antenna array elements is greater than a half wavelength, and the second fitness function is used to indicate at least two optimization objectives; A first determining unit is configured to determine, according to the at least two optimization objectives indicated by the second fitness function, a target position sequence satisfying the second constraint condition according to a plurality of second position sequences through the genetic algorithm; The second determining unit is used to determine the multiple antenna array elements arranged according to the target position sequence as the target antenna array of the MIMO radar.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 11 when executed by an electronic device.

14. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 11 through the computer program.

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