Low, slow and small target detection method, device and equipment based on dynamic threshold factor

Abstract

The application provides a low, slow and small target detection method, device and equipment based on a dynamic threshold factor, which comprises the following steps: performing A / D sampling, digital quadrature phase detection and pulse compression processing on a signal received by a radar antenna to obtain a pulse compression IQ signal; filtering the pulse compression IQ signal by using a first Doppler filter set to obtain a first frequency band signal; filtering the pulse compression IQ signal by using a second Doppler filter set to obtain a second frequency band signal; filtering the first frequency band signal by using a Kalman filter and performing clutter map detection to obtain first track information; filtering the second frequency band signal by using a finite impulse response filter and performing constant false alarm rate detection to obtain second track information; performing track association and target display based on the first track information and the second track information; and dynamically adjusting a threshold factor of the constant false alarm rate detection according to a result of the track association, so that the detection probability of a low, slow and small target and the detection performance of a radar are improved.

Description

Technical Field

[0001] This invention relates to the field of target detection technology, specifically to a method, apparatus, and device for detecting small, slow targets based on a dynamic threshold factor. Background Technology

[0002] In recent years, with the development of drone technology, various types of drones have been widely used in both military and civilian fields. While bringing speed and convenience, the widespread use of drones has also brought certain safety hazards and threats, making people realize the necessity of effectively controlling low-altitude, slow-moving, and small targets such as drones to eliminate low-altitude safety risks. Radar is typically used to detect low-altitude, slow-moving, and small targets; therefore, continuously improving radar's detection capabilities for these targets is of great significance.

[0003] Clutter is a significant factor affecting radar detection performance. Constant False Alarm Rate (CFAR) detection is a commonly used radar detection technique that maximizes target detection probability while maintaining a constant false alarm probability by suppressing clutter. However, in practical engineering, the number of reference cells for estimating clutter levels is limited, making it difficult for CFAR detection methods with fixed threshold factors to guarantee that the false alarm probability is at the desired level. Summary of the Invention

[0004] This invention provides a method, apparatus, and device for detecting small, slow targets based on a dynamic threshold factor, in order to solve the problems existing in the CFAR detection method with a fixed threshold factor.

[0005] In a first aspect, embodiments of the present invention provide a method for detecting small, slow targets based on a dynamic threshold factor, comprising:

[0006] The signal received by the radar antenna is subjected to A / D sampling, digital quadrature phase detection and pulse compression processing to obtain the pulse compression IQ signal;

[0007] The pulse compression IQ signal is filtered using the first Doppler filter bank to obtain the first frequency band signal, which is a clutter frequency band signal;

[0008] The pulse compression IQ signal is filtered by a second Doppler filter bank to obtain a second frequency band signal. The frequency bands of the second frequency band signal do not intersect with those of the first frequency band signal and cover the entire frequency band of the pulse repetition frequency.

[0009] The first frequency band signal is filtered using a Kalmas filter and clutter map is detected to obtain the first spot information;

[0010] Filter the second-band signal using a finite impulse response filter and perform constant false alarm rate detection to obtain the second trace information;

[0011] Perform point-track association and target display based on the first trace information and the second trace information;

[0012] Dynamically adjust the threshold factor of the constant false alarm rate detection according to the result of the point-track association.

[0013] In one embodiment, dynamically adjusting the threshold factor of the constant false alarm rate detection according to the result of the point-track association includes:

[0014] After establishing the target track, obtain the un-tracked traces and the tracked traces from the target track;

[0015] Statistically analyze the signal-to-noise ratios of the un-tracked traces and the tracked traces in distance segments and Doppler segments, and dynamically adjust the threshold factor of the constant false alarm rate detection according to the statistical results.

[0016] In one embodiment, statistically analyzing the signal-to-noise ratios of the un-tracked traces and the tracked traces in distance segments and Doppler segments, and dynamically adjusting the threshold factor of the constant false alarm rate detection according to the statistical results includes:

[0017] Determine the threshold factor of the constant false alarm rate detection according to the following expression K :

[0018]

[0019] where, A is the average signal-to-noise ratio after removing the sample values with signal-to-noise ratios greater than twice their standard deviations in the un-tracked traces within each distance segment and each Doppler segment; B is the minimum signal-to-noise ratio after removing the sample values with signal-to-noise ratios greater than twice their standard deviations in the tracked traces within each distance segment and Doppler segment.

[0020] In one embodiment, the threshold factor is greater than or equal to a preset lower limit value and less than or equal to a preset upper limit value.

[0021] In one embodiment, the finite impulse response filter covers between a×PRF and (1 - a)×PRF, where 0 < a < 1 and PRF represents the pulse repetition frequency.

[0022] In one embodiment, performing the constant false alarm rate detection includes:

[0023] Perform detections using different types of constant false alarm rate detections respectively to obtain multiple detection results;

[0024] Determine the target detection result according to the multiple detection results.

[0025] In one embodiment, different types of constant false alarm rate (CFAR) detection are used to perform the detection, resulting in multiple detection results, including:

[0026] The first detection result was obtained by using the unit average constant false alarm rate detection.

[0027] The second detection result was obtained by using ordered statistical constant false alarm rate detection.

[0028] The third detection result was obtained by using the maximum selection constant false alarm rate (CFAR) detection method.

[0029] The target detection result is determined based on multiple detection results, including:

[0030] The common part of the first, second, and third detection results is determined as the target detection result.

[0031] Secondly, embodiments of the present invention provide a low-speed, small target detection device based on a dynamic threshold factor, comprising:

[0032] The receiving module is used to perform A / D sampling, digital quadrature phase detection, and pulse compression processing on the signal received by the radar antenna to obtain the pulse compression IQ signal.

[0033] The first filtering module is used to filter the pulse compression IQ signal using the first Doppler filter bank to obtain the first frequency band signal, which is a clutter frequency band signal.

[0034] The second filtering module is used to filter the pulse compression IQ signal using the second Doppler filter bank to obtain the second frequency band signal. The frequency bands of the second frequency band signal do not intersect with those of the first frequency band signal and cover the entire frequency band of the pulse repetition frequency.

[0035] The first detection module is used to filter the first frequency band signal with a Kalmas filter and perform clutter map detection to obtain the first point information;

[0036] The second detection module is used to filter the second frequency band signal using a finite impulse response filter and perform constant false alarm rate detection to obtain the second spot information;

[0037] The associated display module is used to perform point navigation association and target display based on the first and second point trace information;

[0038] The dynamic adjustment module is used to dynamically adjust the threshold factor for constant false alarm rate detection based on the results of point navigation association.

[0039] Thirdly, embodiments of the present invention provide an electronic device, comprising:

[0040] At least one processor and memory;

[0041] The memory stores instructions that the computer executes;

[0042] At least one processor executes computer execution instructions stored in memory, causing the at least one processor to perform the low-slow-small target detection method based on dynamic threshold factor as described in any of the first aspects.

[0043] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the low-slow-small target detection method based on a dynamic threshold factor as described in any of the first aspects.

[0044] The present invention provides a method, apparatus, and device for detecting low-speed, small targets based on a dynamic threshold factor. This method involves performing A / D sampling, digital quadrature phase detection, and pulse compression processing on the signal received by the radar antenna to obtain a pulse compression IQ signal. A first Doppler filter bank is used to filter the pulse compression IQ signal to obtain a first frequency band signal, which is a clutter frequency band signal. A second Doppler filter bank is used to filter the pulse compression IQ signal to obtain a second frequency band signal, whose frequency bands do not intersect with the first frequency band signal and cover the entire pulse repetition frequency band. A Kalmas filter is used to filter the first frequency band signal and perform clutter map detection to obtain first point information. A finite impulse response filter is used to filter the second frequency band signal and perform constant false alarm rate (CFAR) detection to obtain second point information. Point navigation association and target display are performed based on the first and second point information. The threshold factor for CFAR detection is dynamically adjusted according to the point navigation association results. The received signal is divided into clutter frequency band signal and other frequency band signal. During MTD, different filters are designed to process the clutter frequency band signal and other frequency band signal separately to ensure that the clutter residue is small during CFAR detection. During CFAR detection, the threshold factor of constant false alarm rate detection is dynamically adjusted according to the result of point navigation association to further improve the detection performance of CFAR detector and ensure that the number of false alarm points is minimized while the target is detected. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] Figure 1 A flowchart of a low-slow-small target detection method based on a dynamic threshold factor provided in an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of a low-speed, small target detection device based on a dynamic threshold factor according to an embodiment of the present invention;

[0048] Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0049] The accompanying drawings have illustrated specific embodiments of the invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0051] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0052] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).

[0053] CFAR (Cross-Clutter Detection) is an important signal processing technique for suppressing clutter and preventing data processors from becoming overloaded due to excessive false alarms. A detection threshold is determined based on clutter intensity to maintain a constant false alarm probability. However, in practical engineering, the number of reference cells for estimating clutter levels is finite. If a theoretical detection threshold factor is still used, it will be difficult to maintain the false alarm probability of CFAR at the desired level. Clutter is a significant factor affecting radar detection performance. Generally, clutter amplitude is strong and widens with skywave beam scanning, resulting in a relatively low Doppler frequency. During Moving Target Detection (MTD) processing, energy leaks into the Doppler channel of small, slow targets, increasing the noise floor and affecting target detection in CFAR. The theoretical CFAR detection threshold factor is a fixed value, but CFAR uses a limited number of reference cells to estimate the size of background clutter. With clutter fluctuations and differences in background clutter intensity across different channels, a fixed threshold factor cannot guarantee that the false alarm probability remains at the desired level. Therefore, a dynamically adjustable CFAR detection threshold factor is of great importance. The following will use specific examples to illustrate in detail the method for detecting small, slow targets based on dynamic threshold factors.

[0054] Figure 1 This is a flowchart illustrating a method for detecting small, slow targets based on a dynamic threshold factor, according to an embodiment of the present invention. Figure 1 As shown, the low-speed small target detection method based on dynamic threshold factor provided in this embodiment may include:

[0055] S101. Perform A / D sampling, digital quadrature phase detection, and pulse compression processing on the signal received by the radar antenna to obtain the pulse compression IQ signal.

[0056] The radar antenna receives an analog signal. This analog signal is then sampled using an analog-to-digital (A / D) converter to obtain a digital signal. Digital quadrature phase detection is then performed on the digital signal to obtain an in-phase quadrature (IQ) signal. This is followed by pulse compression processing to obtain the pulse-compressed IQ signal. It is understandable that the received signal can also undergo digital down-conversion, decimation, and other processing, which will not be elaborated upon here.

[0057] S102. The pulse compression IQ signal is filtered by the first Doppler filter bank to obtain the first frequency band signal, which is the clutter frequency band signal.

[0058] In this embodiment, the first Doppler filter bank can employ a low-Doppler channel, allowing only low-speed clutter signals to pass through. Therefore, after filtering the pulse compression IQ signal using the Doppler filter bank, a clutter frequency band signal will be obtained.

[0059] S103. Filter the pulse compression IQ signal using a second Doppler filter bank to obtain a second frequency band signal. The frequency band of the second frequency band signal does not intersect with that of the first frequency band signal and covers the entire frequency band of the pulse repetition frequency.

[0060] In this embodiment, the second Doppler filter bank can use other Doppler channels except the low Doppler channel in step S102. After filtering the pulse compression IQ signal using the second Doppler filter bank, a second frequency band signal other than the clutter frequency band signal will be obtained. The frequency band of the second frequency band signal does not intersect with that of the first frequency band signal and covers the entire frequency band of the pulse repetition frequency (PRF). The entire frequency band of PRF includes 0 to PRF.

[0061] S104. Filter the first frequency band signal using a Karhunen-Loeve filter and perform clutter map detection to obtain the first track information.

[0062] In this embodiment, first filter the first frequency band signal using a Karhunen-Loeve filter, and then perform clutter map detection on the filtered signal. The Karhunen-Loeve filter is a filter that forms a "zero point" at zero frequency and maintains a flat passband performance outside zero frequency. It can be implemented using two complex filters with a conjugate relationship, and its transfer function is

[0063]

[0064] Among them, H(f) The term can be implemented using a Doppler filter based on the discrete Fourier transform (DFT). In actual engineering processing, the frequency shift operation and windowing coefficient can also be pre-counted into the weighting factor to make it meet the desired main-to-side lobe ratio and amplitude-frequency response of the filter. For the low-channel data processed by the Karhunen-Loeve filter, the clutter has been greatly suppressed, but there is still a certain amount of clutter residue. At this time, using the clutter map detection method for detection helps to further improve the detection accuracy.

[0065] S105. Filter the second frequency band signal using a finite impulse response filter and perform constant false alarm rate detection to obtain the second track information.

[0066] In this embodiment, a finite impulse response (FIR) filter is used. The FIR filter covers between a×PRF and (1 - a)×PRF, ensuring that the main lobe gain of the amplitude-frequency characteristic of the FIR filter bank after low sidelobe windowing is consistent and the sidelobe level is equivalent. Among them, 0 < a < 1, and PRF represents the pulse repetition frequency.

[0067] The echo signal processed by the FIR filter needs to be extracted by a CFAR detector. Under stringent target detection conditions, point information extraction can be achieved through multi-mode CFAR detection. This multi-mode CFAR detection is implemented by performing independent detection using different types of CFAR, and then ANDing the results of each CFAR to obtain the final target detection result. For example, mode 1 is the cell average constant false alarm rate (CA-CFAR), mode 2 is the ordered statistical constant false alarm rate (OS-CFAR), and mode 3 is the maximum selected constant false alarm rate (GO-CFAR). The final result is the common detection portion of modes 1, 2, and 3, while for special target detection, only the detection result of one mode is selected. For low, slow, and small target detection, a cross CFAR detection method can be used, employing CA / OS-CFAR in the range dimension and CA-CFAR in the Doppler dimension. CA-CFAR is used for conventional targets, and OS-CFAR is used for multiple targets. When the target signal meets the threshold requirements in both the range and azimuth dimensions, it is confirmed as a target; otherwise, it is considered as a targetless target.

[0068] In other words, in one optional implementation, constant false alarm rate (CFAR) detection may include: performing detection using different types of CFAR detection to obtain multiple detection results; and determining the target detection result based on the multiple detection results.

[0069] Specifically, different types of constant false alarm rate (CFAR) detection are used to perform detection, resulting in multiple detection results, including: obtaining a first detection result using unit average CFAR detection; obtaining a second detection result using ordered statistical CFAR detection; obtaining a third detection result using maximum selection CFAR detection; and determining the target detection result based on the multiple detection results, including: determining the common part of the first, second, and third detection results as the target detection result.

[0070] S106. Perform point navigation association and target display based on the first point trace information and the second point trace information.

[0071] The second point trace information output by the CFAR detector and the first point trace information output by the clutter map detection are integrated and processed to perform point navigation association and target display.

[0072] S107. Dynamically adjust the threshold factor for constant false alarm rate detection based on the results of point navigation association.

[0073] In CFAR detection, the CFAR detection threshold factor can be initially designed as a theoretical value, and the formula for calculating this value is typically as follows:

[0074]

[0075] in, M Number of reference unitsP fa This represents the probability of a false alarm. K This is the threshold factor.

[0076] After the radar is powered on, the initial CFAR detection threshold factor is configured as the theoretical detection factor. After the radar establishes a stable target track through point-to-point navigation association, the constant false alarm rate (CFAR) detection threshold factor is dynamically adjusted based on the results of the point-to-point navigation association. K The signal-to-noise ratio (SNR) of successful track points and the SNR of track points that failed to establish a track in history (e.g., time k-5 to k-3) are extracted from the target track quality. The SNR of the remaining clutter in the clutter map is also statistically analyzed in real time for the range segment and the Doppler segment. The SNR values ​​after segmented statistical analysis are then sent to the CFAR detector, which dynamically adjusts the threshold factor for the range segment and the Doppler segment.

[0077] The low-speed, small target detection method based on dynamic threshold factor provided in this embodiment obtains a pulse compression IQ signal by performing A / D sampling, digital quadrature phase detection, and pulse compression processing on the signal received by the radar antenna. A first Doppler filter bank is used to filter the pulse compression IQ signal to obtain a first frequency band signal, which is a clutter frequency band signal. A second Doppler filter bank is used to filter the pulse compression IQ signal to obtain a second frequency band signal, whose frequency bands do not intersect with the first frequency band signal and cover the entire pulse repetition frequency band. A Kalmas filter is used to filter the first frequency band signal and perform clutter map detection to obtain first point information. A finite impulse response filter is used to filter the second frequency band signal and perform constant false alarm rate (CFAR) detection to obtain second point information. Point navigation association and target display are performed based on the first and second point information. The threshold factor for CFAR detection is dynamically adjusted according to the result of point navigation association. The received signal is divided into clutter frequency band signal and other frequency band signal. During MTD, different filters are designed to process the clutter frequency band signal and other frequency band signal separately to ensure that the clutter residue is small during CFAR detection. During CFAR detection, the threshold factor of constant false alarm rate detection is dynamically adjusted according to the result of point navigation association to further improve the detection performance of CFAR detector and ensure that the number of false alarm points is minimized while the target is detected.

[0078] Based on the above embodiments, the following details how to dynamically adjust the threshold factor. After the radar establishes a stable target track, the signal-to-noise ratio (SNR) of successfully established tracks and the SNR of tracks that failed to establish tracks in the past (times k-5 to k-3), as well as the SNR of remaining clutter in the clutter map, are statistically analyzed in real time for range and Doppler segments. The segmented SNR values ​​are then sent to the CFAR detector, which dynamically adjusts the threshold factor for each range and Doppler segment. When determining the dynamic threshold factor, firstly, SNR samples of unestablished tracks within each range / Doppler segment with SNR values ​​greater than twice the standard deviation are removed, and then the average SNR is calculated as A. Similarly, SNR samples of established tracks within each range / Doppler segment with SNR values ​​greater than twice the standard deviation are removed, and then the minimum SNR is calculated as B. The threshold factor for CFAR detection is then determined based on A and B.

[0079] Based on the above embodiments, the low-speed small target detection method based on dynamic threshold factor provided in this embodiment dynamically adjusts the threshold factor of constant false alarm rate detection according to the result of point-way association. Specifically, it may include: after establishing the target track, obtaining unestablished and established point tracks from the target track; statistically analyzing the signal-to-noise ratio of unestablished and established point tracks in the range segment and Doppler segment, and dynamically adjusting the threshold factor of constant false alarm rate detection according to the statistical results.

[0080] Specifically, the signal-to-noise ratio (SNR) of unestablished and established waypoints is statistically analyzed in both range and Doppler segments, and the threshold factor for constant false alarm rate (CFAR) detection is dynamically adjusted based on the statistical results, including:

[0081] The threshold factor for constant false alarm rate (CFAR) detection is determined using the following expression. K :

[0082]

[0083] in, A The average signal-to-noise ratio (SNR) after removing samples with an SNR greater than twice the standard deviation from the unestablished waypoint tracks in each distance segment and Doppler segment. B This is the minimum signal-to-noise ratio (SNR) value after removing samples from the built-in waypoint tracks within each range and Doppler segment whose SNR exceeds twice the standard deviation. Furthermore, K The value should be constrained, with an upper limit and a lower limit. That is, the threshold factor should be greater than or equal to the preset lower limit and less than or equal to the preset upper limit.

[0084] In summary, the low-speed, small target detection method based on dynamic threshold factor provided in this application effectively suppresses strong ground clutter and improves the detection probability of target signals in low Doppler channels, making it suitable for detecting slow, small targets in strong clutter backgrounds. Furthermore, the detection of low-speed, small targets based on dynamic CFAR threshold factor improves radar CFAR detection performance. The threshold factor is not configured manually using a trial-and-error method, but rather adaptively and dynamically adjusted under different environments, reducing the presence of residual clutter points.

[0085] Figure 2 This is a schematic diagram of a low-speed, small target detection device based on a dynamic threshold factor, provided in an embodiment of the present invention. Figure 2 As shown, the low-speed small target detection device 20 based on dynamic threshold factor provided in this embodiment may include:

[0086] The receiving module 201 is used to perform A / D sampling, digital quadrature phase detection and pulse compression processing on the signal received by the radar antenna to obtain the pulse compression IQ signal;

[0087] The first filtering module 202 is used to filter the pulse compression IQ signal using the first Doppler filter bank to obtain the first frequency band signal, which is a clutter frequency band signal;

[0088] The second filtering module 203 is used to filter the pulse compression IQ signal using the second Doppler filter bank to obtain the second frequency band signal. The frequency bands of the second frequency band signal do not intersect with those of the first frequency band signal and cover the entire frequency band of the pulse repetition frequency.

[0089] The first detection module 204 is used to filter the first frequency band signal with a Kalmas filter and perform clutter map detection to obtain the first spot information;

[0090] The second detection module 205 is used to filter the second frequency band signal with a finite impulse response filter and perform constant false alarm rate detection to obtain the second spot information;

[0091] The associated display module 206 is used to perform point navigation association and target display based on the first point trace information and the second point trace information;

[0092] The dynamic adjustment module 207 is used to dynamically adjust the threshold factor of the constant false alarm rate detection based on the result of point navigation association.

[0093] The apparatus of this embodiment can be used to perform Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0094] In an optional implementation, the dynamic adjustment module 207 is configured to dynamically adjust the threshold factor of the constant false alarm rate detection according to the result of the point-navigation association, which may specifically include:

[0095] After establishing the target track, obtain the un-tracked point traces and tracked point traces from the target track;

[0096] Statistically analyze the signal-to-noise ratios of the un-tracked point traces and tracked point traces by distance segment and Doppler segment, and dynamically adjust the threshold factor of the constant false alarm rate detection according to the statistical results.

[0097] In an optional implementation, the dynamic adjustment module 207 is configured to statistically analyze the signal-to-noise ratios of the un-tracked point traces and tracked point traces by distance segment and Doppler segment, and dynamically adjust the threshold factor of the constant false alarm rate detection according to the statistical results, which may specifically include:

[0098] Determine the threshold factor of the constant false alarm rate detection according to the following expression K :

[0099]

[0100] where A is the average signal-to-noise ratio after excluding the sample values with signal-to-noise ratios greater than twice their standard deviations in the un-tracked point traces within each distance segment and each Doppler segment; B is the minimum signal-to-noise ratio after excluding the sample values with signal-to-noise ratios greater than twice their standard deviations in the tracked point traces within each distance segment and Doppler segment.

[0101] In an optional implementation, the threshold factor is greater than or equal to a preset lower limit value and less than or equal to a preset upper limit value.

[0102] In an optional implementation, the finite impulse response filter covers between a×PRF and (1 - a)×PRF, where 0 < a < 1 and PRF represents the pulse repetition frequency.

[0103] In an optional implementation, the second detection module 205 is configured to perform constant false alarm rate detection, which may specifically include:

[0104] Perform detections using different types of constant false alarm rate detections respectively to obtain multiple detection results;

[0105] Determine the target detection result according to the multiple detection results.

[0106] In an optional implementation, the second detection module 205 is configured to perform detections using different types of constant false alarm rate detections respectively to obtain multiple detection results, which may specifically include:

[0107] Obtain the first detection result using the cell-averaging constant false alarm rate detection;

[0108] The second detection result was obtained by using ordered statistical constant false alarm rate detection.

[0109] The third detection result was obtained by using the maximum selection constant false alarm rate (CFAR) detection method.

[0110] The second detection module 205 is used to determine the target detection result based on multiple detection results, and may specifically include:

[0111] The common part of the first, second, and third detection results is determined as the target detection result.

[0112] This invention also provides an electronic device, please refer to [link to relevant documentation]. Figure 3 As shown, the embodiments of the present invention are only used as examples. Figure 3 The examples are provided for illustration only and do not imply that the invention is limited to these examples. Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Figure 3 As shown, the electronic device 30 provided in this embodiment includes: a memory 301, a processor 302, and a bus 303. The bus 303 is used to connect the various components.

[0113] The memory 301 stores a computer program, which, when executed by the processor 302, can implement the technical solutions of any of the above method embodiments.

[0114] The memory 301 and the processor 302 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines, such as bus 303. The memory 301 stores a computer program that implements a low-speed, small target detection method based on a dynamic threshold factor, including at least one software functional module that can be stored in the memory 301 in the form of software or firmware. The processor 302 executes various functional applications and data processing by running the software program and modules stored in the memory 301.

[0115] The memory 301 may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 301 stores programs, and the processor 302 executes the programs after receiving execution instructions. Furthermore, the software programs and modules within the memory 301 may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components.

[0116] Processor 302 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 302 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. It is understood that... Figure 3 The structure shown is for illustrative purposes only and may include more... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown. Figure 3 The components shown can be implemented in hardware and / or software.

[0117] This invention also provides a computer-readable storage medium storing a computer program thereon, which is executed by a processor to implement the technical solutions of any of the above method embodiments.

[0118] The various embodiments in this disclosure are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0119] The scope of protection of this disclosure is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its scope and spirit. If such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A method for detecting small, slow targets based on a dynamic threshold factor, characterized in that, Comprising: Performing A / D sampling, digital quadrature phase discrimination, and pulse compression processing on the signals received by the radar antenna to obtain pulse-compressed IQ signals; Filtering the pulse-compressed IQ signals using a first Doppler filter bank to obtain a first frequency band signal, where the first frequency band signal is a clutter frequency band signal; Filtering the pulse-compressed IQ signals using a second Doppler filter bank to obtain a second frequency band signal, where the second frequency band signal and the first frequency band signal do not intersect in frequency band and cover the entire frequency band of the pulse repetition frequency; Filtering the first frequency band signal using a Kalman filter and performing clutter map detection to obtain first trace information; Filtering the second frequency band signal using a finite impulse response filter and performing constant false alarm rate detection to obtain second trace information; Performing point-track association and target display based on the first trace information and the second trace information; Dynamically adjusting the threshold factor of the constant false alarm rate detection according to the result of the point-track association.

2. The method according to claim 1, characterized in that, The dynamically adjusting the threshold factor of the constant false alarm rate detection according to the result of the point-track association includes: After establishing a target track, obtaining un-tracked points and tracked points from the target track; Statistically analyzing the signal-to-noise ratios of the un-tracked points and tracked points in distance segments and Doppler segments, and dynamically adjusting the threshold factor of the constant false alarm rate detection according to the statistical results.

3. The method according to claim 2, characterized in that, The statistically analyzing the signal-to-noise ratios of the un-tracked points and tracked points in distance segments and Doppler segments, and dynamically adjusting the threshold factor of the constant false alarm rate detection according to the statistical results includes: Determining the threshold factor K of the constant false alarm rate detection according to the following expression: where A is the average signal-to-noise ratio after removing the sample values with signal-to-noise ratios greater than twice their standard deviations in the un-tracked points within each distance segment and each Doppler segment; B is the minimum signal-to-noise ratio value after removing the sample values with signal-to-noise ratios greater than twice their standard deviations in the tracked points within each distance segment and Doppler segment.

4. The method according to claim 3, characterized in that, The threshold factor is greater than or equal to a preset lower limit value and less than or equal to a preset upper limit value.

5. The method according to claim 1, characterized in that, The finite impulse response filter covers between a×PRF and (1 - a)×PRF, where 0 < a < 1 and PRF represents the pulse repetition frequency.

6. The method according to claim 1, characterized in that, The performing the constant false alarm rate detection includes: Performing detections using different types of constant false alarm rate detections respectively to obtain multiple detection results; Determining the target detection result according to the multiple detection results.

7. The method according to claim 6, wherein The performing detections using different types of constant false alarm rate detections respectively to obtain multiple detection results includes: Obtaining a first detection result using cell-averaging constant false alarm rate detection; Obtaining a second detection result using ordered-statistic constant false alarm rate detection; Obtaining a third detection result using greatest-of-constant false alarm rate detection; The determining the target detection result according to the multiple detection results includes: Determining the common part of the first detection result, the second detection result, and the third detection result as the target detection result.

8. A low-speed, small target detection device based on a dynamic threshold factor, characterized in that, Comprising: A receiving module for performing A / D sampling, digital quadrature phase discrimination, and pulse compression processing on the signals received by the radar antenna to obtain pulse-compressed IQ signals; The first filtering module is used to filter the pulse compression IQ signal using a first Doppler filter bank to obtain a first frequency band signal, wherein the first frequency band signal is a clutter frequency band signal; The second filtering module is used to filter the pulse compression IQ signal using a second Doppler filter bank to obtain a second frequency band signal. The frequency bands of the second frequency band signal do not intersect with those of the first frequency band signal and cover the entire frequency band of the pulse repetition frequency. The first detection module is used to filter the first frequency band signal with a Kalmas filter and perform clutter map detection to obtain the first spot information; The second detection module is used to filter the second frequency band signal using a finite impulse response filter and perform constant false alarm rate detection to obtain the second spot information; The associated display module is used to perform point navigation association and target display based on the first point trace information and the second point trace information; The dynamic adjustment module is used to dynamically adjust the threshold factor for constant false alarm rate detection based on the results of point navigation association.

9. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the low-slow-small target detection method based on a dynamic threshold factor as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the low-slow-small target detection method based on a dynamic threshold factor as described in any one of claims 1-7.

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