A method and system for deploying sparse array antennas for ultra-wideband positioning.

Abstract

A method and system for arranging sparse array antennas for ultra-wideband (UWB) positioning are disclosed, relating to the field of UWB signal modulation. This invention addresses the problem that hardware limitations in the transmission and reception of UWB signals significantly reduce the effective range and accuracy of UWB signal location estimation, resulting in a large discrepancy between practical application and theoretical limits. The method includes: matching antenna elements according to the desired UWB signal; solving for the main lobe gain of the array antenna based on the desired maximum radar range and radar equations; optimizing the main lobe gain and sidelobes of the array antenna using a genetic algorithm to obtain the array's positional distribution; and arranging the array sparsely according to the positional distribution. This invention is applied to the field of radar ranging.

Description

Technical Field

[0001] This invention relates to the field of ultra-wideband signal modulation, and more particularly to a method for arranging sparse array antennas for ultra-wideband positioning. Background Technology

[0002] The biggest difference between ultra-wideband (UWB) signals and traditional narrowband and wideband signals lies in their extremely large bandwidth. According to the Federal Communications Commission's definition of UWB signals, an absolute bandwidth greater than 500 MHz or a relative bandwidth greater than 20% of the signal's bandwidth. Because of this very large bandwidth, UWB signals can be implemented as waveforms with very short durations, which is highly beneficial for accurate distance and location estimation.

[0003] However, in practical applications, the transmission and reception of ultra-wideband (UWB) signals are limited by hardware, which significantly reduces the effective range and accuracy of UWB signal location estimation, resulting in a large gap between practical application and theoretical limits. For example, omnidirectional antennas are typically used for UWB signal location estimation. The advantage of omnidirectional antennas is their ability to receive UWB echo signals from all directions. However, their disadvantage is lower gain, which greatly limits the effective positioning range of UWB signals. Furthermore, because omnidirectional antennas also receive multipath reflections, multiple similar echo peaks can occur, significantly impacting the accuracy of UWB positioning. Summary of the Invention

[0004] This invention addresses the hardware limitations inherent in the transmission and reception of ultra-wideband (UWB) signals, which significantly reduces the effective distance and accuracy of UWB signal location estimation, resulting in a large discrepancy between practical application and theoretical limits. To address this issue, a method for arranging sparse array antennas for UWB positioning is proposed, comprising:

[0005] Match the antenna elements according to the desired ultra-wideband signal;

[0006] The main lobe gain of the array antenna is calculated based on the desired maximum radar range and the radar equations.

[0007] The main lobe gain and side lobes of the array antenna are optimized using a genetic algorithm to obtain the array's positional distribution.

[0008] Sparse array arrangement is performed based on the positional distribution of the array.

[0009] Furthermore, a preferred embodiment is provided in which the antenna element matched according to the desired ultra-wideband signal is a directional antenna.

[0010] Furthermore, a preferred embodiment is provided, wherein the maximum radar effective range is:

[0011] The maximum radar range R is obtained based on key radar parameters and the target radar cross-section. max :

[0012]

[0013] Among them, P t It is the transmit power, measured in watts (W); G is the antenna gain; A e It is the effective aperture of the antenna, measured in meters (m). 2 σ is the radar cross-sectional area of ​​the target, in meters. 2 S min It is the smallest detectable signal strength, measured in W.

[0014] Furthermore, a preferred embodiment is also provided, wherein optimizing the main lobe gain and side lobes of the array antenna using a genetic algorithm to obtain the array's positional distribution includes:

[0015] Generate an initial population and sort the genes of each individual in ascending order;

[0016] The sorted individual genes are transformed into the population with the actual distance interval, and fitness is calculated.

[0017] Determine if the fitness of the population with the true distance interval meets the termination condition; if the termination condition is met, output the optimal individual.

[0018] If the termination condition is not met, selection, crossover, and mutation operations are performed. Individuals in the intermediate population are reordered according to their gene size from smallest to largest, and then transformed into a population with a true distance interval. For the evolved sub-band population, the termination condition is re-evaluated until the termination condition is met and output is performed.

[0019] Furthermore, a preferred method is also provided, wherein the selection operation is as follows:

[0020] Utilizing each individual f i,g The proportion of fitness determines the likelihood of an individual's offspring being retained; if an individual f i,g The fitness is fit, and the population size is N. p Then the probability p of it being selected is... i Represented as:

[0021]

[0022] The greater an individual's fitness, the greater its chance of being selected.

[0023] Furthermore, a preferred embodiment is also provided, wherein the crossover operation includes:

[0024] Select the odd number of individuals f 2i-1,g and even number of individuals f2i,g Pairing is performed, and for each pair of individuals, the crossover probability P is used. c Perform cross operations to form a new pair of individuals.

[0025] Furthermore, a preferred embodiment is also provided, wherein the mutation operation includes:

[0026] In the population after crossover, a random number r is generated from j = 1 to N, i = 1 to NP, in the interval [0, 1]. If the random number is less than the mutation probability P, then... m If the (j, i)th gene f(j, i) is selected as the mutated gene;

[0027] The original gene is replaced by a parameter from a randomly generated value range, as shown in the following formula:

[0028] y ji = rand[0,1]·(L-(N) Y -1)d c )

[0029] z ji = rand[0,1]•(H-(N) Z -1)d c )

[0030] Where rand[0,1] represents a randomly generated value uniformly distributed within [0,1], L is the length of the antenna array, and N... Y Let N be the Nth element in the Y direction, and H be the width of the antenna array. Z For the nth element in the width direction, d c To limit the minimum element spacing, y ji Let z be the initial position of the array element along the length of the array. ji This represents the initial position of the array elements along the width direction of the array.

[0031] Based on the same inventive concept, this invention also proposes an array system for sparse array antennas used in ultra-wideband positioning, the system comprising:

[0032] Matching unit, used to match antenna elements according to the desired ultra-wideband signal;

[0033] The solver unit is used to solve for the main lobe gain of the array antenna based on the desired maximum radar range and radar equations.

[0034] The optimization unit is used to optimize the main lobe gain and side lobes of the array antenna according to the genetic algorithm, and to obtain the position distribution of the array;

[0035] The arrangement unit is used to perform sparse array arrangement according to the positional distribution of the array.

[0036] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium for storing a computer program that executes a sparse array antenna arrangement method for ultra-wideband positioning as described in any of the preceding claims.

[0037] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes an arraying method for a sparse array antenna for ultra-wideband positioning according to any one of the preceding claims.

[0038] The advantages of this invention are:

[0039] This invention solves the problem that the transmission and reception of ultra-wideband signals are limited by hardware, which leads to a significant reduction in the effective distance and accuracy of the estimated location of ultra-wideband signals, resulting in a large gap between the actual use and the theoretical limit.

[0040] The present invention discloses a sparse array antenna arrangement method for ultra-wideband positioning. Using an antenna array with a sparse layout in ultra-wideband signal positioning can greatly improve the effective positioning distance. In addition, the sparse array antenna has a lower sidelobe level, which can effectively suppress the interference of multipath echoes and improve the positioning accuracy of ultra-wideband signals.

[0041] This invention discloses a sparse array antenna arrangement method for ultra-wideband (UWB) positioning. This method constructs an UWB antenna array by arranging UWB antenna elements with directional radiation capabilities in a specific pattern. By addressing both the antenna elements and the array configuration, the antenna gain performance is improved. Therefore, the effective positioning distance of UWB signals can be increased without increasing the system's transmit power.

[0042] The present invention discloses a sparse array antenna arrangement method for ultra-wideband positioning. This method uses a sparse array synthesis method based on a genetic algorithm to optimize the ultra-wideband antenna array. It can reduce the number of array elements used while keeping the array pattern gain unchanged, thereby reducing system complexity and lowering costs.

[0043] The present invention discloses a sparse array antenna arrangement method for ultra-wideband positioning. Based on a genetic algorithm, the sparse layout of the ultra-wideband antenna array elements is optimized. With the low sidelobe level of the array antenna as the optimization target, the sidelobe level of the antenna array pattern can be reduced while achieving high gain, thereby reducing the influence of multipath echo interference and improving positioning accuracy.

[0044] This invention discloses a sparse array antenna arrangement method for ultra-wideband (UWB) positioning. Addressing the hardware limitations inherent in UWB signal transmission and reception, this method overcomes these limitations by optimizing the array layout, thereby improving signal transmission distance and reception accuracy. By optimizing the array layout and main lobe gain, the accuracy of the positioning system can be improved, making the actual positioning results closer to the theoretical limits. Compared to dense arrays, sparse arrays require fewer antenna elements, reducing hardware costs and complexity. By selecting appropriate antenna elements and optimizing the array's positional distribution, the array's reception and transmission performance in specific directions can be optimized, thus improving the positioning accuracy and effective range of UWB signals.

[0045] This invention is applied to the field of radar ranging. Attached Figure Description

[0046] Figure 1 This is a flowchart of a sparse array antenna arrangement method for ultra-wideband positioning as described in Embodiment 1.

[0047] Figure 2 This is a schematic diagram of the matching bandwidth of the ultra-wideband antenna element described in Embodiment 2, where Voltage Standing Wave Ratio (VSWR) represents the standing wave ratio and Frequency represents the frequency.

[0048] Figure 3 The flowchart for optimizing the main lobe gain and side lobes of the array antenna using a genetic algorithm, as described in Implementation Method 4; Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0050] Implementation Method 1, see [link] Figure 1 This embodiment describes a method for arranging a sparse array antenna for ultra-wideband positioning. The method includes:

[0051] Match the antenna elements according to the desired ultra-wideband signal;

[0052] The main lobe gain of the array antenna is calculated based on the desired maximum radar range and the radar equations.

[0053] The main lobe gain and side lobes of the array antenna are optimized using a genetic algorithm to obtain the array's positional distribution.

[0054] Sparse array arrangement is performed based on the positional distribution of the array.

[0055] The method described in this embodiment first selects suitable antenna elements based on the desired ultra-wideband signal. This is to ensure that the antenna can effectively receive and transmit the ultra-wideband signal. The main lobe gain of the array antenna is calculated based on the desired maximum radar range and radar equations. The main lobe gain refers to the ratio of the antenna's radiated power in a specific direction to its omnidirectional radiation. It affects the signal transmission distance and reception accuracy. A genetic algorithm is used to optimize the main lobe gain and sidelobes of the array antenna. A genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms and can be used to find the optimal solution. Here, it is used to optimize the array's positional distribution to optimize signal transmission and reception performance. Based on the optimized array positional distribution, a sparse array antenna is arranged. A sparse array antenna refers to a group of antenna elements distributed at certain intervals in space, with a larger spacing compared to a dense array.

[0056] This implementation addresses the hardware limitations inherent in ultra-wideband (UWB) signal transmission and reception by optimizing array layout to overcome these limitations, thereby improving signal transmission distance and reception accuracy. Optimizing array layout and main lobe gain enhances the accuracy of the positioning system, bringing actual positioning results closer to theoretical limits. Compared to dense arrays, sparse arrays require fewer antenna elements, reducing hardware cost and complexity. By selecting appropriate antenna elements and optimizing array placement, the array's reception and transmission performance in specific directions can be optimized, thus improving the positioning accuracy and effective range of UWB signals.

[0057] Implementation Method 2, see below Figure 2 This embodiment further defines the sparse array antenna arrangement method for ultra-wideband positioning described in Embodiment 1, wherein the antenna element matched according to the desired ultra-wideband signal is a directional antenna.

[0058] To improve the radiation performance of ultra-wideband signals in a specific direction, this embodiment selects a directional antenna with good matching performance. Compared to omnidirectional antennas, directional antennas have significantly better radiation performance in a particular direction than in other directions, and can provide higher gain in that direction. In this embodiment, the ultra-wideband channels 4 and 5 under the IEEE standard are targeted, corresponding to frequencies of 4243MHz to 4742MHz. Therefore, the selected ultra-wideband antenna should have good matching performance in this frequency band. The VSWR parameters of the ultra-wideband antenna used are as follows: Figure 2 As shown, its VSWR parameter is less than 2 in the 4.2GHz-4.8GHz range, indicating that it has good matching performance in this range and can meet the matching requirements of ultra-wideband channel 4 and channel 5 under the IEEE standard.

[0059] This implementation uses directional antenna elements to concentrate the antenna's radiation and reception performance in a specific direction, reducing interference and background noise in directions of non-interest, and improving signal anti-interference and positioning accuracy. Directional antennas typically have higher gain because they focus more energy in a specific direction, thereby increasing signal transmission distance and receiving sensitivity. Compared to omnidirectional antennas, directional antennas generally require less energy to transmit and receive signals, which reduces battery consumption and extends device lifespan.

[0060] This implementation further optimizes the ultra-wideband positioning system to improve its performance. By selecting a directional antenna as the basic element, the radiation and reception direction of the signal can be better controlled, thereby locating the target more accurately, reducing errors, and minimizing aliasing and multipath interference during signal propagation. A directional antenna is an antenna element designed to have maximum response in a specific direction. They typically employ a narrow beamwidth, meaning they only receive or radiate signals from a specific direction. In principle, the performance of a directional antenna is achieved through its directivity and radiation characteristics. The principle behind selecting directional antenna basic elements is that they allow the system to better control the direction of received and transmitted signals, thus better meeting the requirements of positioning applications. This control can be achieved through the antenna's physical shape, directivity, and beamwidth to ensure that the signal is primarily concentrated in the region of interest, reducing background noise and interference. This contributes to improving the performance and reliability of the positioning system.

[0061] Implementation Method 3: This implementation method further defines the sparse array antenna deployment method for ultra-wideband positioning described in Implementation Method 1, wherein the maximum radar operating range is:

[0062] The maximum radar range R is obtained based on key radar parameters and the target radar cross-section. max :

[0063]

[0064] Among them, P t It is the transmit power, measured in watts (W); G is the antenna gain; A e It is the effective aperture of the antenna, measured in meters (m). 2 σ is the radar cross-sectional area of ​​the target, in meters. 2 S min It is the smallest detectable signal strength, measured in W.

[0065] The purpose of calculating the maximum radar range in this implementation is to determine the farthest distance at which the radar system can effectively detect a target. This is crucial for the localization of ultra-wideband signals, as it guides the design of antenna placement and signal processing. Determining the maximum range by considering radar parameters and target characteristics contributes to system performance optimization.

[0066] This implementation method, by calculating the maximum radar range, ensures that the radar system has a sufficient signal-to-noise ratio at the maximum range to reliably detect targets. This helps improve system performance and availability.

[0067] Implementation Method Four, see below Figure 3 This embodiment describes a further limitation on the sparse array antenna deployment method for ultra-wideband positioning described in Embodiment 1. The step of optimizing the main lobe gain and sidelobes of the array antenna using a genetic algorithm to obtain the array's positional distribution includes:

[0068] Generate an initial population and sort the genes of each individual in ascending order;

[0069] The sorted individual genes are transformed into the population with the actual distance interval, and fitness is calculated.

[0070] Determine if the fitness of the population with the true distance interval meets the termination condition; if the termination condition is met, output the optimal individual.

[0071] If the termination condition is not met, selection, crossover, and mutation operations are performed. Individuals in the intermediate population are reordered according to their gene size from smallest to largest, and then transformed into a population with a true distance interval. For the evolved sub-band population, the termination condition is re-evaluated until the termination condition is met and output is performed.

[0072] This implementation uses a genetic algorithm to find the optimal array antenna position distribution to optimize main lobe gain and reduce sidelobes. This is to improve the performance of the ultra-wideband positioning system, enhance signal directivity, and suppress unwanted interference. First, an initial set of individuals (array antenna position distribution) is generated. Each individual has a set of genes representing the array's position distribution. The genes of each individual are arranged in ascending order, and these genes are then mapped to the population at the actual range intervals. Next, the fitness of each individual is calculated, typically by evaluating main lobe gain and sidelobes using a radar performance model. The fitness of the population at the actual range intervals is checked to see if a termination condition is met. If it is, the optimal individual, i.e., the array layout with the best performance, is output. If not, the next step is performed. If the termination condition is not met, a selection operation is performed, choosing individuals with higher fitness. Then, a crossover operation is performed, combining the genes of different individuals to generate new individuals. Finally, a mutation operation is performed, introducing small-amplitude random changes to the genes. This process is repeated until the termination condition is met. In each generation, individuals in the intermediate population are again sorted by gene size from smallest to largest, then mapped to the population with the actual distance interval. Fitness is recalculated, and selection, crossover, and mutation operations continue. Through repeated iterations, the genetic algorithm finds a better-performing array antenna layout to optimize main lobe gain and reduce sidelobes, thereby improving the performance of the ultra-wideband positioning system. The advantage of this method lies in its ability to find the global optimum in a complex problem space and its adaptability, enabling it to adapt to different problems and constraints.

[0073] Implementation Method 5: This implementation method further defines the sparse array antenna arrangement method for ultra-wideband positioning described in Implementation Method 1. The selection operation is as follows:

[0074] Utilizing each individual f i,g The proportion of fitness determines the likelihood of an individual's offspring being retained; if an individual f i,g The fitness is fit, and the population size is N. p Then the probability p of it being selected is... i Represented as:

[0075]

[0076] The greater an individual's fitness, the greater its chance of being selected.

[0077] The primary objective of the selection operation in this implementation is to determine which individuals will be retained to participate in subsequent reproduction and the generation of a new generation. This is to pass on more promising solutions to the next generation based on an individual's fitness, gradually improving the population's performance. By proportionalizing the selection probability to fitness, superior individuals are more likely to be selected, thus having the opportunity to pass on their advantageous genetic traits to the next generation. This helps accelerate the population's evolution towards a better solution. The selection operation is similar to natural selection in nature, where individuals better adapted to the environment are more likely to reproduce. This natural selection mechanism helps the population gradually adapt to the problem space, improving overall performance. Although more adapted individuals have a greater chance of being selected, the selection operation still retains a degree of diversity, as even individuals with lower fitness have a chance to be selected. This helps avoid getting trapped in local optima.

[0078] Implementation Method Six: This implementation method further defines the sparse array antenna deployment method for ultra-wideband positioning described in Implementation Method Four. The crossover operation includes:

[0079] Select the odd number of individuals f 2i-1,g and even number of individuals f 2i,g Pairing is performed, and for each pair of individuals, the crossover probability P is used. c Perform cross operations to form a new pair of individuals.

[0080] The primary purpose of the crossover operation in this implementation is to introduce diversity and optimize the population by combining the genetic information of two different individuals to generate new individuals. This helps increase genetic variation within the population, aiding in the search for better solutions. By performing crossover on different individuals, the genetic algorithm introduces diversity, as each parent individual may possess different strengths and weaknesses. This helps prevent the population from getting trapped in local optima, as different genetic traits have the opportunity to combine, producing offspring with greater potential. The crossover operation allows the advantages of different individuals to be combined, thereby creating new individuals with better adaptability. This helps accelerate the evolution of the population towards a better solution.

[0081] Implementation Method Seven: This implementation method further defines the sparse array antenna arrangement method for ultra-wideband positioning described in Implementation Method Six. The variation operation includes:

[0082] In the population after crossover, a random number r is generated from j = 1 to N, i = 1 to NP, in the interval [0, 1]. If the random number is less than the mutation probability P, then... m If the (j, i)th gene f(j, i) is selected as the mutated gene;

[0083] The original gene is replaced by a parameter from a randomly generated value range, as shown in the following formula:

[0084] y ji = rand[0,1]·(L-(N) Y -1)d c )

[0085] z ji = rand[0,1]·(H-(N) Z -1)d c )

[0086] Where rand[0,1] represents a randomly generated value uniformly distributed within [0,1], L is the length of the antenna array, and N... Y Let N be the Nth element in the Y direction, and H be the width of the antenna array. Z For the nth element in the width direction, d c To limit the minimum element spacing, y ji Let z be the initial position of the array element along the length of the array. ji This represents the initial position of the array elements along the width direction of the array.

[0087] The primary purpose of mutation in this implementation is to maintain population diversity to avoid premature convergence to local optima. By introducing a small amount of randomness into individual genes, mutation helps to explore a wider region of the search space, increasing the chances of finding the global optimum. Introducing randomness into individual genes helps to explore different parts of the search space, including regions that may contain better solutions. This helps overcome early population convergence because it allows the population to continuously try new variants. Mutation helps maintain population diversity because it generates differences between individuals in the population, rather than making them tend to be similar. Diversity is crucial for the performance of genetic algorithms because it can help the algorithm escape local optima.

[0088] Implementation Method Eight: A sparse array antenna deployment system for ultra-wideband positioning, as described in this implementation method, comprising:

[0089] Matching unit, used to match antenna elements according to the desired ultra-wideband signal;

[0090] The solver unit is used to solve for the main lobe gain of the array antenna based on the desired maximum radar range and radar equations.

[0091] The optimization unit is used to optimize the main lobe gain and side lobes of the array antenna according to the genetic algorithm, and to obtain the position distribution of the array;

[0092] The arrangement unit is used to perform sparse array arrangement according to the positional distribution of the array.

[0093] Implementation Method Nine: A computer-readable storage medium according to this implementation method, the computer-readable storage medium being used to store a computer program, the computer program executing an arraying method for a sparse array antenna for ultra-wideband positioning as described in any one of Implementation Methods One to Seven.

[0094] Implementation Method 10: A computer device according to this implementation method includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a sparse array antenna deployment method for ultra-wideband positioning according to any one of Implementation Methods 1 to 7.

[0095] Implementation Method Eleven: This implementation method provides a specific embodiment of the sparse array antenna arrangement method for ultra-wideband positioning described in Implementation Method One, and also serves to explain Implementation Methods One to Seven. Specifically:

[0096] This embodiment addresses the ranging problem of ultra-wideband signals by proposing a method using a sparse array antenna to improve the effective ranging range and accuracy of ultra-wideband signals. The flowchart of this method is shown below. Figure 1 As shown, firstly, antenna elements matching the expected ultra-wideband signal are determined so that the signal can radiate into free space. Then, based on the required maximum effective communication distance, the gain performance of the array antenna's main lobe is calculated according to the radar equations. After solving for the array antenna gain, a genetic algorithm is used to perform sparse layout synthesis of the array. By optimizing the position of the array elements, the expected gain performance is achieved, while simultaneously suppressing the sidelobe level of the array antenna pattern. After sparse optimization, the array's position distribution can be obtained, and sparse array fabrication can be performed based on this distribution. Using an antenna array with a sparse layout in ultra-wideband signal positioning can significantly improve the effective positioning distance. Furthermore, sparse array antennas have lower sidelobe levels, which can effectively suppress multipath echo interference and improve the positioning accuracy of ultra-wideband signals.

[0097] Specifically, the positioning distance for ultra-wideband (UWB) signals ranges from tens to hundreds of meters. If an omnidirectional antenna is used, after the UWB signal source emits electromagnetic waves, the waves radiate in all directions of free space with a spherical wavefront. Because the emitted energy is omnidirectionally radiated, the energy radiated in a particular direction is lower; in the antenna field, this is called directivity, and the directivity coefficient is often referred to as the array's directional gain. This parameter assesses the concentration of energy radiated by the array antenna. It is defined as the ratio of the power density in the main lobe direction to the power density of the omnidirectional antenna, given a fixed total power of electromagnetic energy radiated by the array antenna. The mathematical definition of this parameter is:

[0098]

[0099] in, It represents the radiation pattern function of the array antenna, F max This is the maximum value of the parameter. The unit of the directivity coefficient is also decibel (dB), and its calculation formula is as follows:

[0100] D dB =10lgD

[0101] To improve the radiation performance of ultra-wideband signals in a specific direction, this embodiment first selects a directional antenna with good matching performance. Compared to omnidirectional antennas, directional antennas are characterized by significantly higher radiation performance in a specific direction compared to other directions, and can provide higher gain in that direction. In this embodiment, the ultra-wideband channels 4 and 5 under the IEEE standard are targeted, corresponding to frequencies of 4243MHz to 4742MHz. Therefore, the selected ultra-wideband antenna should have good matching performance in this frequency band. The VSWR parameters of the ultra-wideband antenna used are as follows: Figure 3 As shown, its VSWR parameter is less than 2 in the 4.2GHz-4.8GHz range, indicating that it has good matching performance in this range and can meet the matching requirements of ultra-wideband channel 4 and channel 5 under the IEEE standard.

[0102] In the second step, the required main lobe gain performance of the sparse array antenna is calculated based on the radar equations, and the maximum radar range R is obtained based on the key radar parameters and the target radar cross-section. max Its expression is shown in the following formula.

[0103]

[0104] Among them, P t It is the transmit power, measured in watts (W); G is the antenna gain; A e It is the effective aperture of the antenna, measured in meters (m). 2 σ is the radar cross-sectional area of ​​the target, in meters. 2 S min It is the smallest detectable signal strength, measured in W;

[0105] In this embodiment, the transmit power of the ultra-wideband signal source, the radar cross-sectional area of ​​the target, and the minimum detectable signal strength can be considered constant. However, after replacing the omnidirectional antenna in the ultra-wideband signal positioning with a sparse array antenna, the effective aperture of the antenna increases, and in this embodiment, it is constrained to be three times the wavelength of 0.0354 corresponding to the lowest frequency of the ultra-wideband signal, 4243MHz. The required gain of the array antenna can then be calculated based on the maximum effective positioning distance.

[0106] Then, a genetic algorithm is used to optimize the element positions of the sparse array antenna. The genetic algorithm synthesis process for sparse array synthesis is as follows: Figure 3 As shown, an initial intermediate population with real-valued genes is first generated. The genes of each individual are sorted in ascending order, and then the population is transformed to the true distance interval. The fitness of each individual in this intermediate population is calculated, and the termination condition is checked. If the condition is met, the algorithm stops and the optimal individual is output as the optimized result. If not, selection, crossover, and mutation operations are performed on the individuals in the intermediate population, and each individual is again sorted in ascending order of genes and transformed to the true distance interval population. The termination condition is checked again for the evolved sub-band population. This process is repeated until the termination condition is met.

[0107] In this implementation, the selection operation uses a "roulette wheel" method, utilizing the individual f i,g The proportion of fitness determines the likelihood of an individual's offspring being retained. If an individual f i,g The fitness is fit, and the population size is N. p The probability of it being selected is expressed as

[0108]

[0109] The greater an individual's fitness, the greater its chance of being selected; conversely, the lower its fitness, the greater its chance of being selected. To select crossover individuals, multiple rounds of selection are required. Each round generates a uniformly random number within the range [0, 1], which is used as a selection pointer to determine the selected individual.

[0110] The crossover operation in this embodiment includes: selecting the odd-numbered individuals f 2i-1,g and even number of individuals f 2i,g Pairing is performed, and for each pair of individuals, the crossover probability P is used. c The process involves exchanging some genes between individuals. The specific steps are: first, select a pair of individuals to be mated; then, based on the bit string length L, randomly select an integer k from [1, L-1] as the crossover point position for the pair; finally, based on the crossover probability P... c Crossover occurs when paired individuals exchange parts of their genes at the crossover point, thus forming a new pair of individuals.

[0111] The mutation operation in this embodiment includes: for each individual f in the crossover population i,g With the mutation probability P mThe gene values ​​at certain loci are changed to other allele values. The specific steps are as follows: In the population after crossover, a random number r is generated from j = 1 to N, i = 1 to NP, within the interval [0, 1]. If r < Pm, then the (j, i)th gene f(j, i) is selected as the gene to be mutated. Then, it is replaced with a parameter from the randomly generated value range, as shown in the following formula:

[0112] y ji = rand[0,1]·(L-(N) Y -1)d c )

[0113] z ji = rand[0,1]·(H-(N) Z -1)d c )

[0114] rand[0, 1] represents a randomly generated value uniformly distributed within [0, 1]. After individual selection, crossover, and gene mutation operations, a new population is obtained. The gene values ​​in the newly generated population need to be mutated again to obtain a new f. i,g .

[0115] via f i,g Find ff i,g ff i,g This is the true distance interval matrix for the array element distribution. To ensure that there are elements at all four corners of the array, a further mutation operation is performed. This is applied to the newly generated ff... i,g Fitness is calculated, and the best individual is retained in the next generation of the population for the next genetic operation. The genetic algorithm terminates after a given number of iterations or when certain conditions are met, and outputs the optimal result.

[0116] After obtaining the positional relationships of each element in the sparse array using a genetic algorithm, the ultra-wideband elements can be filled into the corresponding aperture according to their distribution, thus forming an ultra-wideband sparse array antenna layout. Using an antenna array with a sparse layout in ultra-wideband signal localization can significantly improve the effective localization distance. In addition, sparse array antennas have lower sidelobe levels, which can effectively suppress multipath echo interference and improve the localization accuracy of ultra-wideband signals.

[0117] The sparse array antenna arrangement method for ultra-wideband positioning described in this embodiment can greatly improve the effective positioning distance by using an antenna array with a sparse layout in ultra-wideband signal positioning. In addition, the sparse array antenna has a lower sidelobe level, which can effectively suppress the interference of multipath echoes and improve the positioning accuracy of ultra-wideband signals.

[0118] This embodiment describes a sparse array antenna arrangement method for ultra-wideband (UWB) positioning. It constructs an UWB antenna array by arranging UWB antenna elements with directional radiation capabilities in a specific pattern. By addressing both the antenna elements and the array configuration, it improves antenna gain performance. Therefore, it can increase the effective positioning distance of UWB signals without increasing the system's transmit power.

[0119] The sparse array antenna arrangement method for ultra-wideband positioning described in this embodiment uses a sparse array synthesis method based on genetic algorithms to optimize the ultra-wideband antenna array. This method can reduce the number of array elements used while keeping the array pattern gain constant, thereby reducing system complexity and lowering costs.

[0120] The sparse array antenna arrangement method for ultra-wideband positioning described in this embodiment optimizes the sparse layout of the ultra-wideband antenna array elements based on a genetic algorithm. With the goal of reducing the sidelobe level of the array antenna, it can reduce the sidelobe level of the antenna array pattern while achieving high gain, thereby reducing the impact of multipath echo interference and improving positioning accuracy.

[0121] The technical solutions provided by the present invention have been described in further detail above with reference to the accompanying drawings in order to highlight their advantages and benefits, and are not intended to limit the present invention. Any modifications, combinations, improvements and equivalent substitutions of the present invention based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for arranging a sparse array antenna for ultra-wideband positioning, characterized in that, The method includes: Match the antenna elements according to the desired ultra-wideband signal; The main lobe gain of the array antenna is calculated based on the desired maximum radar range and the radar equations. The main lobe gain and side lobes of the array antenna are optimized using a genetic algorithm to obtain the array's positional distribution. Sparse array arrangement is performed based on the positional distribution of the array; The antenna element matched according to the desired ultra-wideband signal is a directional antenna; The maximum radar range is: Maximum radar range is obtained from key radar parameters and target radar cross section : ， wherein, is the transmitted power in W; is the antenna gain; is the effective aperture of the antenna in m2; ; is the radar cross section of the target in m2; ; is the minimum detectable signal strength in W; The optimization of the main lobe gain and side lobes of the array antenna using a genetic algorithm to obtain the array's positional distribution includes: Generate an initial population and sort the genes of each individual in ascending order; The sorted individual genes are transformed into the population with the actual distance interval, and fitness is calculated. Determine if the fitness of the population with the true distance interval meets the termination condition; if the termination condition is met, output the optimal individual. If the termination condition is not met, the selection, crossover and mutation operations are performed. The individuals in the intermediate population are sorted again according to the gene size from smallest to largest, and then transformed into a population with a real distance interval. For the evolved sub-band population, the termination condition is re-evaluated until the termination condition is met and then output. The selection operation is as follows: Utilizing individual The size of the proportion of fitness determines the likelihood of offspring retention of an individual, if the fitness of a certain individual is , and the population size is , then the probability of being selected is represented as: The greater an individual's fitness, the greater its chance of being selected; The crossover operation includes: Select the odd number of individuals and even number of individuals Pairing is performed, and for each pair of individuals, the crossover probability is used. Perform cross operations to form a new pair of individuals; The mutation operation includes: In the population after crossover, from j = 1 to N, i = 1 to N P Generate a random number r in the interval [0, 1]. If the random number is less than the mutation probability... If the (j,i)th gene f(j,i) is selected as the mutated gene; The original gene is replaced by a parameter from a randomly generated value range, as shown in the following formula: Where rand[0,1] represents a randomly generated value uniformly distributed within [0,1]. The length of the antenna array, The Nth element in the Y direction, The width of the antenna array. The Nth element in the width direction, Minimum element spacing constraint, The initial positions of the array elements along the length of the array. This represents the initial position of the array elements along the width direction of the array.

2. A sparse array antenna deployment system for ultra-wideband positioning, characterized in that, The system is implemented based on the method of claim 1, and the system includes: Matching unit, used to match antenna elements according to the desired ultra-wideband signal; The solver unit is used to solve for the main lobe gain of the array antenna based on the desired maximum radar range and radar equations. The optimization unit is used to optimize the main lobe gain and side lobes of the array antenna according to the genetic algorithm, and to obtain the position distribution of the array; The arrangement unit is used to perform sparse array arrangement according to the positional distribution of the array.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that executes the arraying method of a sparse array antenna for ultra-wideband positioning as described in claim 1.

4. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a sparse array antenna deployment method for ultra-wideband positioning as described in claim 1.

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