Radar altimeter data processing method and device and storage medium

Abstract

The invention discloses a radar altimeter data processing method and device and a storage medium, and relates to the technical field of radar altimeter data processing. The method comprises the following steps: carrying out processing and target detection on all echo signals to obtain potential target points; dividing M pulse echo data in one frame into a plurality of data segments with different accumulation lengths; for each potential target point, calculating coherent accumulation energy of the potential target point in each data segment; according to the change rule of coherent accumulation energy of the potential target points among the data segments, incoherent interference target points are discriminated and filtered out; sorting the residual target points after filtering according to energy, and selecting Q target points with the highest energy for tracking; and comparing the energy of each target tracking chain in the historical frame, and taking the distance value corresponding to the target tracking chain with the highest energy as the final output value of the radar altimeter. According to the invention, data jump is greatly suppressed from a data source, and the problem of lack of capability of suppressing power supply fluctuation and electromagnetic interference is solved.

Description

Technical Field

[0001] This invention belongs to the field of radar altimeter data processing technology, and particularly relates to a radar altimeter data processing method, device and storage medium. Background Technology

[0002] For low-altitude aircraft, especially drones, accurate and stable altitude perception is crucial when performing tasks such as surveying, logistics, inspection, and urban flight. Radar altimeters, as core altitude-keeping sensors, directly measure the relative altitude between the aircraft and the ground by transmitting and receiving electromagnetic waves. The accuracy of this data directly affects the stability of flight control and flight safety.

[0003] However, in practical applications, the output data of existing radar altimeters is susceptible to interference from various internal and external uncertainties, leading to problems such as jumps in altitude hold data and over-range errors. If this erroneous altitude information is adopted by the flight control system, it can easily trigger malfunctions in the aircraft, such as abnormal climbs or dives, ultimately causing the aircraft to lose control or even crash. Current radar altimeters face the following key challenges in their application:

[0004] (1) The complex electromagnetic environment inside the aircraft constitutes a serious source of interference.

[0005] Aircraft integrate various communication modules, sensors, and power conversion devices. The strong, non-periodic electromagnetic interference generated by their motors during operation can cause radar to misinterpret the interference signal as a target point in the signal spectrum, resulting in abnormal abrupt changes in the output altitude information. Although radar hardware designs typically incorporate certain electromagnetic protection measures, the interference spectrum, intensity, and type vary greatly among different aircraft, making it difficult for a single hardware solution to effectively suppress all potential uncertainties and interference.

[0006] (2) The unstable airborne power supply system of the aircraft introduces power interference.

[0007] Aircraft are powered by batteries, and during drastic attitude adjustments or sudden acceleration maneuvers, the power supply system experiences significant fluctuations, potentially resulting in momentary undervoltage, overvoltage, or power pulses. If these power interferences are not effectively suppressed, they will directly affect the normal operation of the radar hardware system, causing jumps in its output data. Currently, existing radar altimeters generally lack specialized detection and suppression capabilities for such power fluctuation interference at the data processing level.

[0008] (3) The complex and ever-changing ground environment poses a severe challenge to radar altimetry.

[0009] When an aircraft operates in uneven terrain or urban environments, there may be sudden towering structures such as skyscrapers or mountains on the ground. Radar altimeters emit beams of a certain width, and when flying over such areas, the beam may simultaneously cover both the ground and buildings. This causes the radar altimeter to repeatedly detect the actual ground height and the building height, resulting in drastic variations in the output data over a wide range.

[0010] Chinese patent document CN201610497842.1 discloses a data processing method for a UAV radar altimeter. It primarily determines the output by comparing the magnitudes of altitude values ​​in adjacent frames and performing statistical classification. In scenarios where ground altitude changes abruptly, this method relies heavily on comparing target values ​​in adjacent frames, making it relatively simplistic. Especially when facing prolonged, fixed electromagnetic interference or power supply interference, the interference signal remains consistently present in each frame. This method may incorrectly identify interference points as valid target points and output them, resulting in insufficient reliability and potentially causing the aircraft to fail to respond in time, thus increasing the risk of collision.

[0011] (4) The echo characteristics (RCS) of radar targets are subject to flickering and the signal-to-noise ratio (SNR) is weakened.

[0012] The radar cross section (RCS) of ground targets varies due to differences in size, shape, and material, and exhibits a flickering characteristic over time, leading to instability in the target echo energy received by radar between frames. Furthermore, when aircraft are flying at extremely high altitudes or over long ranges, or when the reflectivity of ground targets is too low, the target signal-to-noise ratio (SNR) can suddenly or continuously weaken. Current technologies, which rely solely on the echo energy magnitude of a single frame or a small number of consecutive frames to distinguish between RCS flickering and SNR weakening caused by over-range operation, lack effective historical management and trend analysis of target echo energy. This makes it highly susceptible to misclassifying low-energy noise points as target points or misinterpreting flickering weak targets as invalid signals, resulting in abnormal altitude information output.

[0013] In summary, existing radar altimeter technology has significant shortcomings, mainly in the following aspects: a) lack of effective suppression mechanisms for power supply fluctuation interference at the data processing level; b) difficulty in coping with complex and variable uncertain electromagnetic interference on aircraft platforms; c) insufficient responsiveness to sudden changes in altitude in real ground environments, easily leading to misjudgments; and d) lack of robust management methods for target RCS flicker and signal-to-noise ratio weakening. Therefore, there is an urgent need for a radar altimeter data processing method and system that can comprehensively address the aforementioned multiple interference sources and possess stronger robustness and environmental adaptability. Summary of the Invention

[0014] The purpose of this invention is to provide a radar altimeter data processing method, device and storage medium, which aims to solve the problems of altitude jump and over-range caused by various internal and external uncertainties in radar altimeters in low-altitude aircraft.

[0015] This invention solves the above-mentioned technical problems through the following technical solution: a radar altimeter data processing method, comprising:

[0016] Receive the echo signal corresponding to each transmitted linearly modulated pulse;

[0017] All echo signals are processed and targets are detected to obtain potential target points; where all echo signals refer to the echo signals received when the radar altimeter sequentially transmits a frame of modulated signals consisting of M linearly modulated pulses.

[0018] The M pulse echo data within a frame are divided into multiple data segments with different accumulation lengths; where, single pulse echo data refers to the digital signal data obtained by mixing and sampling the echo signal corresponding to a single linear modulation pulse.

[0019] For each potential target point, calculate its coherent cumulative energy within each data segment;

[0020] Based on the variation pattern of coherent accumulated energy of potential target points between data segments, identify and filter out incoherent interfering target points;

[0021] The remaining target points after filtering are sorted by energy, and the Q target points with the highest energy are selected for tracking, forming Q target tracking chains;

[0022] By comparing the energy of each target tracking chain in historical frames, the distance value corresponding to the target tracking chain with the highest energy is taken as the final output value of the radar altimeter.

[0023] In this embodiment, based on the fundamental characteristics that the echo and transmitted signal of the real target originate from the same source, while the interference and transmitted signal do not originate from the same source, by dividing the data into data segments of different accumulation lengths and checking whether the energy of the target point conforms to the coherent accumulation gain law, all interference that does not conform to the coherent accumulation gain law is fundamentally identified and filtered out. Regardless of whether these interferences originate from the electromagnetic environment or power fluctuations, as long as their incoherent nature remains unchanged, they can be effectively eliminated, directly solving the data jump problem caused by electromagnetic interference and power interference at the data source.

[0024] This invention simultaneously tracks the Q highest-energy targets, establishing and maintaining an independent target tracking chain for each target, and comparing the energy of each target tracking chain across historical frames. When terrain changes abruptly, the energy of the high-rise target tracking chain remains consistently high. After accumulating over multiple frames, its historical average energy exceeds that of the flat-ground target chain, thus switching the output to the target chain representing the higher altitude. This method can quickly respond to real-world environmental changes while avoiding erroneous switching caused by single-frame flicker, solving the problem of insufficient responsiveness to real-world height changes.

[0025] When a real ground target experiences a temporary energy drop in a frame due to RCS flicker, this invention, based on the historical energy of the target tracking chain, is likely to maintain its average energy at its highest level, thus ensuring continued output and preventing target loss due to temporary energy decline. This significantly enhances the tracking robustness for flickering targets and effectively distinguishes between transient RCS flicker and permanent signal-to-noise ratio loss.

[0026] Furthermore, all echo signals are processed and target detection is performed, specifically including:

[0027] The echo signal corresponding to the linear modulation pulse being transmitted at the current moment is mixed with the linear modulation pulse transmitted at the previous moment to obtain an analog intermediate frequency signal.

[0028] During the duration of the linear modulation pulse being transmitted at the current moment, the analog intermediate frequency signal is sampled to obtain K sampling points;

[0029] All sampling points acquired within a frame of modulated signal are arranged in order to form a K×M original data matrix;

[0030] A two-dimensional fast Fourier transform is performed on the original data matrix to obtain a K×M dimensional distance-Doppler matrix;

[0031] Target detection is performed on the distance-Doppler matrix to obtain the potential target points.

[0032] In this embodiment, the original analog echo signal is converted into a digital matrix containing target distance and velocity information, and potential target points are initially detected to provide input for subsequent processing.

[0033] Furthermore, the coherent accumulation energy of the potential target point within each data segment is calculated, specifically including:

[0034] Determine the corresponding range gate based on the position of the potential target point in the range-Doppler matrix;

[0035] For each data segment, extract the complex signal value corresponding to the distance gate of the potential target point from the data segment;

[0036] Perform complex vector summation on all complex signal values ​​within the data segment;

[0037] The square of the modulus of the sum of complex vectors is calculated, and this result is used as the coherent accumulated energy of the potential target point within the data segment.

[0038] Furthermore, based on the variation pattern of coherent accumulated energy of potential target points across data segments, incoherent interfering target points are identified and filtered out, specifically including:

[0039] Calculate the coherent accumulation energy gain of potential target points between data segments with longer accumulation lengths and data segments with shorter accumulation lengths;

[0040] The coherent accumulation energy gain is compared with a preset threshold range determined based on the coherent accumulation gain theory;

[0041] If the coherent accumulated energy gain meets the requirements of the preset threshold range, the potential target point is determined to be a coherent signal from the same source and is retained; otherwise, it is determined to be an incoherent interference signal and is filtered out.

[0042] Furthermore, the number of data segments is at least three, and their accumulated lengths are multiples of each other; the discrimination and filtering step includes:

[0043] Calculate the coherent accumulation energy gain between adjacent data segments where the accumulation length is a multiple of each other for the potential target point, and obtain multiple coherent accumulation energy gains;

[0044] Count the number of coherent accumulated energy gains that fall within a preset threshold range;

[0045] If the number of counts reaches or exceeds the preset qualified quantity threshold, the potential target point is determined to be a coherent signal from the same source and is retained; otherwise, the potential target point is determined to be an incoherent interference signal and is filtered out.

[0046] Furthermore, the nearest neighbor algorithm is used to cluster the Q target points across frames, forming Q target tracking chains, specifically including:

[0047] A target tracking chain is established and maintained for each tracked target to record the target's state information in multiple consecutive frames;

[0048] Based on the state information of each target tracking chain in the previous frame, predict its predicted state in the current frame.

[0049] Calculate the association cost between the state information of Q targets in the current frame and the predicted state of Q target tracking chains;

[0050] For each target tracking chain, find the target point with the minimum association cost with its predicted state from the Q targets in the current frame and associate it with it;

[0051] The status information of successfully associated target points is updated to the corresponding target tracking chain to form the target tracking chain for the current frame.

[0052] In this implementation, the nearest neighbor algorithm performs cross-frame point clustering to form target chains, upgrading data processing from static, passive filtering to dynamic, active tracking. This greatly enhances robustness against transient interference and target RCS flicker, effectively suppresses height data jumps, and provides smooth and stable height output.

[0053] Furthermore, the method also includes a target attention mechanism, the specific steps of which include:

[0054] Real-time monitoring of the signal-to-noise ratio of the target tracking chain corresponding to the final output value;

[0055] When the signal-to-noise ratio is lower than a first preset threshold, the target attention mechanism is triggered;

[0056] If the target attention mechanism is triggered within p consecutive frames, it is determined that the effective detection range of the radar altimeter has been exceeded.

[0057] When it is determined that the target is outside the effective detection range, the confidence level of the height value output by the radar altimeter is set to zero, and the output remains the effective height value of the last frame before the target attention mechanism is triggered.

[0058] In this embodiment, the target attention mechanism actively monitors whether the radar altimeter has reached the detection range, and proactively acknowledges failure and issues an alarm when the detection range is reached, thus avoiding guiding the aircraft to make incorrect decisions; continuous p-frame judgment avoids misjudgment caused by instantaneous signal fluctuations; and the data retention strategy provides the flight control system with crucial decision-making and switching time.

[0059] Furthermore, the method also includes an environmental detection and judgment step, specifically including:

[0060] When the target tracking chain corresponding to the final output value undergoes a sudden numbering change, environmental detection is triggered.

[0061] Calculate the root mean square average of the energy of all FFT points in the distance dimension of the current frame;

[0062] Calculate the difference between the root mean square value and the historical background energy;

[0063] If the difference exceeds the second preset threshold, it is determined to be a real environmental change, and a new height value is confirmed and output; if the difference does not exceed the second preset threshold, it is determined to be an erroneous jump, and the new height value is rejected.

[0064] The historical background energy is the statistical value of the root mean square average of the most recent L frames before the mutation occurred.

[0065] In this embodiment, by cross-validating local mutations and global background, the system accurately distinguishes between real-world environmental mutations and erroneous jumps caused by interference, thereby enabling timely and correct responses to changes in the ground environment, guiding the aircraft to make the right decisions, and effectively avoiding collision risks.

[0066] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the radar altimeter data processing method as described above.

[0067] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the radar altimeter data processing method as described above.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] The method of this invention does not rely on the threshold judgment of interference intensity, but judges and filters out interference by checking whether the growth law of signal energy with the increase of accumulated pulse number conforms to the coherent accumulation theory. It can uniformly and effectively filter out various internal (such as power fluctuations) and external (such as electromagnetic) interferences from different sources than radar transmitted signals, greatly suppressing data jumps from the data source and solving the problem of lack of ability to suppress power fluctuations and electromagnetic interference.

[0070] This invention upgrades data processing from static, single-frame point filtering to dynamic, cross-frame target tracking chains, improving tolerance to target RCS flicker and brief interference, and enhancing the smoothness and stability of output height values.

[0071] This invention proposes a target attention mechanism that intelligently distinguishes between transient signal fading and permanent signal loss (such as exceeding the range) by monitoring the signal-to-noise ratio and introducing delayed decision logic. Upon confirmation of failure, a "failure-safety" degradation mode is achieved through a strategy of "zeroing the confidence level + maintaining the altitude value." This prevents aircraft malfunctions caused by outputting random noise data at performance boundaries and provides valuable buffer time for the flight control system to switch to backup sensors, significantly enhancing the overall system safety.

[0072] This invention uses the root mean square energy method for environmental detection and judgment, ignoring a few isolated peaks and capturing the macroscopic characteristics of the background reflection intensity of the entire detection scene. This provides a reliable basis for determining whether the environment has undergone fundamental changes, solves the problem of distinguishing between real environmental abrupt changes and erroneous data jumps, and ensures the accuracy and intelligence of decision-making in complex environments. Attached Figure Description

[0073] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a flowchart of the radar altimeter data processing method in an embodiment of the present invention;

[0075] Figure 2 This is a schematic diagram of frequency-modulated continuous wave radar waveform modulation in an embodiment of the present invention;

[0076] Figure 3 This is a spectral diagram of the angular anti-target range of the radar altimeter against a background of strong reflectors in an embodiment of the present invention;

[0077] Figure 4 This is a spectral diagram of the angular reflective target range of the radar altimeter in an embodiment of the present invention against a background of weak reflectivity. Detailed Implementation

[0078] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0080] Example 1

[0081] like Figure 1 As shown, the radar altimeter data processing method provided in this embodiment includes the following steps:

[0082] Step 1: Receive the echo signal corresponding to each transmitted linearly modulated pulse.

[0083] Radar altimeter according to Figure 2 The modulation method generates a frame of modulated signal. For example... Figure 2 As shown, a single frame of modulated signal consists of M linear modulation pulses (i.e., M chirp signals). The fast time corresponds to the duration of a single chirp signal, and the slow time corresponds to the duration of a frame of modulated signal. For example, a frame of modulated signal consists of 128 linear modulation pulses.

[0084] Radar altimeter according to Figure 2 M linear modulation pulses are transmitted sequentially, and the echo signals corresponding to each transmitted linear modulation pulse are received sequentially to obtain all the echo signals of the current frame, that is, the echo signals corresponding to the M transmitted linear modulation pulses.

[0085] Step 2: Process all echo signals and perform target detection to obtain potential target points.

[0086] In a specific embodiment of the present invention, processing and target detection of all echo signals specifically includes:

[0087] Step 2.1: Mix the echo signal corresponding to the linear modulation pulse being transmitted at the current moment with the linear modulation pulse transmitted at the previous moment to obtain the analog intermediate frequency signal;

[0088] Step 2.2: During the duration of the linear modulation pulse being transmitted at the current moment (i.e., one fast time), use an analog-to-digital converter to sample the analog intermediate frequency signal to obtain K sampling points (e.g., 256 points are collected in one fast time).

[0089] Step 2.3: Arrange all the sampling points acquired within a frame of modulated signal in order to form a K×M original data matrix;

[0090] Step 2.4: Perform a two-dimensional fast Fourier transform on the original data matrix to obtain a K×M dimensional distance-Doppler matrix;

[0091] Step 2.5: Perform target detection on the range-Doppler matrix to obtain all potential target points, and number all potential target points.

[0092] In this embodiment, performing a two-dimensional fast Fourier transform on the original data matrix includes:

[0093] Perform a Fast Fourier Transform (FFT) on each column (i.e., each independent Chirp) of the original data matrix to obtain an intermediate matrix;

[0094] Perform a Fast Fourier Transform (FFT) on each row of the intermediate matrix (i.e., each distance gate) to obtain the distance-Doppler matrix.

[0095] Range-dimensional FFT transforms a signal from the fast time domain to the range-frequency domain. Each point after the FFT (i.e., the range gate) corresponds to a specific range cell, and the amplitude of that point reflects the presence of a target and its signal strength at that distance. Velocity-dimensional FFT transforms a signal from the slow time domain to the Doppler frequency domain. Each point after the FFT corresponds to a specific velocity cell, and the amplitude of that point reflects the presence of a target at that velocity and range combination.

[0096] In step 2.5, the cell-averaged constant false alarm rate (CA-CAFR) method is used to detect targets on the range-Doppler matrix, with a detection threshold set to 5 dB. The cell-averaged CAFR method is a constant false alarm rate detection method that uses background noise power as the threshold. Because the threshold uses background noise power, it ensures a constant false alarm probability. Setting the threshold to a relatively low 5 dB means higher detection sensitivity, ensuring that as many real targets as possible (even weak targets) can be initially detected and then proceed to subsequent judgment processes, thus improving the radar's ability to detect weak targets.

[0097] Step 3: Divide the M pulse echo data within a frame into multiple data segments with different accumulation lengths.

[0098] Single-pulse echo data refers to the digital signal data obtained after mixing and sampling the echo signal corresponding to a single linear modulation pulse, i.e., the data obtained in step 2.3. An accumulation length gradient was created by segmentation to observe the growth law of signal energy with the increase of the number of accumulation pulses, which is the basis for subsequent discrimination.

[0099] For example, when M=128, the 128 pulse echo data are divided into data segments with accumulation lengths of 16, 32, 64, and 128 respectively. Data segment A with an accumulation length of 16 includes the first 16 pulse echo data, data segment B with an accumulation length of 32 includes the first 32 pulse echo data, data segment C with an accumulation length of 64 includes the first 64 pulse echo data, and data segment D with an accumulation length of 128 includes all pulse echo data.

[0100] Step 4: For each potential target point, calculate its coherent cumulative energy in each data segment.

[0101] In a specific embodiment of the present invention, for each potential target point, the coherent accumulation energy within each data segment is calculated, specifically including:

[0102] Step 4.1: Determine the corresponding range gate based on the position of the potential target point in the range-Doppler matrix;

[0103] Step 4.2: For each data segment, extract the complex signal value (including amplitude and phase information) corresponding to the distance gate of the potential target point from the data segment.

[0104] Step 4.3: Perform complex vector summation on all complex signal values ​​within the data segment, i.e., coherent accumulation;

[0105] Step 4.4: Calculate the square of the modulus of the sum of the complex vectors, and use this calculation result as the coherent accumulated energy of the potential target point in the data segment.

[0106] Step 5: Based on the variation pattern of coherent accumulated energy of potential target points between data segments, identify and filter out incoherent interference target points.

[0107] In a specific embodiment of the present invention, based on the variation law of the coherent accumulated energy of potential target points between data segments, incoherent interfering target points are identified and filtered out, specifically including:

[0108] Step 5.1: Calculate the coherent accumulation energy gain of the potential target point between data segments with longer accumulation lengths and data segments with shorter accumulation lengths;

[0109] Step 5.2: Compare the coherent accumulation energy gain with a preset threshold range determined based on the coherent accumulation gain theory;

[0110] If the coherent accumulation energy gain is within the preset threshold range, the potential target point is determined to be a coherent signal from the same source and is retained; otherwise, it is determined to be an incoherent interference signal and is filtered out.

[0111] In a specific embodiment of the present invention, the number of data segments is at least three, and the accumulated lengths are multiples of each other; the identification and filtering steps include:

[0112] Calculate the coherent accumulated energy gain of potential target points between adjacent data segments whose accumulated lengths are multiples of each other, and obtain multiple coherent accumulated energy gains; count the number of multiple coherent accumulated energy gains that fall into a preset threshold range; if the count reaches or exceeds the preset qualified number threshold, the potential target point is determined to be a coherent signal from the same source and is retained; otherwise, the potential target point is determined to be an incoherent interference signal and is filtered out.

[0113] In this embodiment, the preset threshold range is [3-δ, 3+δ]dB, where δ represents the tolerance value, which can be set to 0.5dB or 1dB. The qualified quantity threshold is the number of coherent accumulated energy gains, that is, if all coherent accumulated energy gains fall within the preset threshold range, then the corresponding potential target point is determined to be a coherent signal from the same source and is retained.

[0114] The formula for coherent accumulation gain is: G d =10logN, where Gd This represents the coherent accumulation gain, and N represents the number of accumulated pulses.

[0115] For example, for the aforementioned four data segments: data segment A with a cumulative length of 16, data segment B with a cumulative length of 32, data segment C with a cumulative length of 64, and data segment D with a cumulative length of 128, we have:

[0116] From data segment A to data segment B, the number of pulses doubles, and the theoretical coherent accumulation energy gain is 10log(32)-10log(16)≈3dB. Therefore, the coherent accumulation energy of data segment B should be about 3dB higher than that of data segment A.

[0117] From data segment B to data segment C, the number of pulses doubles, and the theoretical coherent accumulation energy gain is 10log(64) - 10log(32) ≈ 3dB. Therefore, the coherent accumulation energy of data segment C should be about 3dB higher than that of data segment B.

[0118] From data segment C to data segment D, the number of pulses doubles, and the theoretical coherent accumulation energy gain is 10log(128) - 10log(64) ≈ 3dB. Therefore, the coherent accumulation energy of data segment D should be about 3dB higher than that of data segment C.

[0119] It can be seen that the energy value of the real target (coherent signal from the same source) will show a stable stepwise increase of approximately 3dB per doubling as the number of accumulated pulses increases.

[0120] The jamming signal and the radar transmitted signal are from different sources, and their phases are random. Therefore, the accumulation across chirps is incoherent (energy addition, not vector addition). In incoherent accumulation, signal power and noise power are linearly superimposed; doubling the number of pulses doubles both the signal power (increases by 3dB) and the noise power (increases by 3dB). Therefore, the signal-to-noise ratio remains constant. That is, the ratios of the coherent accumulation energy of data segment B to that of data segment A, C to B, and D to C are all close to 1 (0dB gain), and a stable 3dB gain step will not appear. Its energy curve will be a relatively flat, irregularly increasing line.

[0121] Therefore, for each potential target point, by examining the growth relationship between its coherent accumulated energy in data segment A, data segment B, data segment C, and data segment D, it can be determined whether the potential target point is an interference signal.

[0122] This invention accurately distinguishes between real targets and interference by checking whether the energy of each potential target point on the spectrum changes by 3dB on the accumulated pulses that are multiples of each other, without the need for complex calculation logic.

[0123] Step 6: Sort the remaining target points after filtering by energy, select the Q target points with the highest energy for tracking, and form Q target tracking chains.

[0124] The remaining target points after filtering are sorted by energy, where energy refers to the signal power of the range-velocity unit corresponding to the target point in the range-Doppler matrix. Q target points (e.g., 3 target points) are numbered, and the energy value of each target in each frame of data is recorded after tracking begins. The nearest neighbor algorithm is used to cluster the Q target points across frames, forming Q target tracking chains. In a specific embodiment of this invention, the nearest neighbor algorithm for clustering the Q target points across frames specifically includes:

[0125] Step 6.1: Establish and maintain a target tracking chain for each tracked target to record the target's state information in multiple consecutive frames;

[0126] Step 6.2: Based on the state information of each target tracking chain in the previous frame, predict its predicted state in the current frame;

[0127] Step 6.3: Calculate the association cost between the state information of the Q targets in the current frame and the predicted state of the Q target tracking chains;

[0128] Step 6.4: For each target tracking chain, find the target point with the minimum association cost with its predicted state from the Q targets in the current frame and associate it with it;

[0129] Step 6.5: Update the status information of the successfully associated target points to the corresponding target tracking chain to form the target tracking chain for the current frame.

[0130] In this embodiment, the predicted state of the target tracking chain in the current frame can be achieved using existing methods such as the Kalman filter. The nearest neighbor algorithm performs cross-frame point clustering to form the target chain, upgrading data processing from static, passive filtering to dynamic, active tracking, which greatly enhances robustness to transient interference and target RCS flicker, effectively suppresses height data jumps, and provides smooth and stable height output.

[0131] Step 7: Compare the energy of each target tracking chain in the historical frames, and take the distance value corresponding to the target tracking chain with the highest energy as the final output value of the radar altimeter.

[0132] In a specific embodiment of the present invention, the processing method further includes a target attention mechanism, the specific steps of which include:

[0133] Step 7.1: Monitor the signal-to-noise ratio of the target tracking chain corresponding to the final output value in real time;

[0134] Step 7.2: When the signal-to-noise ratio is lower than the first preset threshold (e.g., 10dB), trigger the target attention mechanism;

[0135] Step 7.3: If the target attention mechanism is triggered within p consecutive frames, it is determined that the effective detection range of the radar altimeter has been exceeded;

[0136] Step 7.4: When it is determined that the effective detection range is exceeded, the confidence level of the radar altimeter output altitude value is set to zero, and the output remains the effective altitude value of the last frame before the target attention mechanism is triggered.

[0137] The target attention mechanism is similar to a status flag. When the target attention mechanism is triggered for p consecutive frames (e.g., 2 frames), it indicates that attention needs to be paid to the validity of the radar altimeter output altitude value. At this time, the confidence of the radar altimeter output altitude value is set to zero, and the valid altitude value of the last frame before the target attention mechanism was triggered is maintained.

[0138] The radar altimeter data processing of this invention is performed in consecutive frames, and the number of the target tracking chain corresponding to the final output value in step 7 is monitored in real time to see if there is a sudden change. The target tracking chain corresponding to the final output value is one of Q target tracking chains. A sudden change in the number of the target tracking chain corresponding to the final output value means that the number of the target tracking chain that is the final output has changed in consecutive frames, and the output altitude value is changed by another target tracking chain in Q target tracking chains. There are two reasons for this sudden change: one is a real and correct environmental change, and the other is an erroneous and dangerous data jump. Therefore, it is necessary to determine the real cause of the sudden change and perform environmental detection and judgment. In a specific embodiment of this invention, environmental detection and judgment specifically include:

[0139] When the target tracking chain corresponding to the final output value undergoes a sudden numbering change, environmental detection is triggered.

[0140] Calculate the root mean square average of the energy of all FFT points in the distance dimension of the current frame;

[0141] Calculate the difference between the root mean square value and the historical background energy;

[0142] If the difference exceeds the second preset threshold, it is determined to be a real environmental change, and a new height value is confirmed and output; if the difference does not exceed the second preset threshold, it is determined to be an erroneous jump, and the new height value is rejected.

[0143] In step 2, when K is 256 and M is 128, the size of the range-Doppler matrix is ​​256×128, and the number of range-dimensional FFT points is 256. After performing a range-dimensional FFT on the echo signal of a radar chirp signal, the signal power corresponding to each range gate in the spectrum is the FFT point energy. The specific formula for the root mean square average of the energy of all FFT points in the range dimension of the current frame is: ,in, represents the root mean square average of the energy of all FFT points in the distance dimension; K represents the number of FFT points in the distance dimension; , … This indicates the energy value of each distance gate.

[0144] The root mean square (RMS) average has the advantage of low sensitivity to sudden outliers, making it more suitable for assessing the overall characteristics of the data and effectively suppressing interfering pulse-like values. By calculating the RMS average of the energy at all FFT points in the distance dimension, a few isolated peaks are ignored, capturing the macroscopic characteristics of the background reflection intensity of the entire detection scene, thus providing a reliable basis for determining whether the environment has undergone fundamental changes.

[0145] Historical background energy serves as a reference or baseline value, representing a representative overall energy level of the radar's reflective environment before any abrupt change in the target tracking chain's serial number. In this embodiment, historical background energy can be set as the root mean square average of the last frame before the abrupt change. Alternatively, it can be set to the root mean square average of the most recent L frames before the mutation occurred. The average or median. In this embodiment, the second preset threshold ranges from 5dB to 15dB.

[0146] Figure 3 and Figure 4 The range spectrum of a corner reflector target at the same distance is shown against strong and weak reflector backgrounds, respectively. Here, label 1 represents the corner reflector target, label 2 represents the ambient noise floor, Y represents the target's distance (in meters), and Z represents the target's energy value (in dB). Figure 3 and Figure 4 As indicated by mark 1, the two targets are at the same distance from the radar altimeter. As indicated by mark 2, the noise floor differs by approximately 12 dB between the two environments.

[0147] The root mean square (RMS) value reflects the overall noise floor performance. The RMS value of energy against a strong reflective background can better reflect changes in the radar altimeter background. For example, if the target detected by the radar altimeter suddenly changes from a land background to a tall building, the environmental detection and judgment of this invention can accurately determine the reflective background of the radar altimeter, precisely distinguish between real environmental changes and erroneous jumps caused by interference, and thus make a timely and correct response to changes in the ground environment.

[0148] Example 2

[0149] This invention also provides an electronic device, which includes a memory, a processor, and a computer program or instructions stored in the memory. The processor executes the computer program or instructions to implement the radar altimeter data processing method in Embodiment 1 of this invention.

[0150] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0151] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0152] Although not shown, embodiments of the present invention also provide a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the radar altimeter data processing method of Embodiment 1 of the present invention.

[0153] Readable storage media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0154] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of processing radar altimeter data, characterized by, The processing method comprises: receiving echo signals corresponding to each transmitted linear modulation pulse; processing and target detection on all echo signals to obtain potential target points; wherein all echo signals refer to echo signals received by the radar altimeter when sequentially transmitting a frame of modulation signals composed of M linear modulation pulses; dividing M pulse echo data in a frame into data segments of different accumulation lengths; wherein single pulse echo data refers to digital signal data obtained by mixing and sampling echo signals corresponding to a single linear modulation pulse; for each potential target point, calculating its coherent accumulation energy in each data segment; according to the change rule of the coherent accumulation energy of the potential target points between data segments, discriminating and filtering out non-coherent interference target points; sorting the remaining target points after filtering according to energy, selecting the Q target points with the highest energy for tracking to form Q target tracking chains; comparing the energy of each target tracking chain in the historical frame, and taking the distance value corresponding to the target tracking chain with the highest energy as the final output value of the radar altimeter.

2. The radar altimeter data processing method of claim 1, wherein, The processing and target detection on all echo signals specifically comprises: mixing the echo signals corresponding to the linear modulation pulse being transmitted at the current moment and the linear modulation pulse having been transmitted at the last moment to obtain an analog intermediate frequency signal; sampling the analog intermediate frequency signal within the duration of the linear modulation pulse being transmitted at the current moment to obtain K sampling points; sequentially arranging all sampling points collected in a frame of modulation signals to form a K×M original data matrix; performing two-dimensional fast Fourier transform on the original data matrix to obtain a K×M distance-Doppler matrix; performing target detection on the distance-Doppler matrix to obtain the potential target points.

3. The radar altimeter data processing method of claim 1, wherein, The calculation of the coherent accumulation energy of the potential target points in each data segment specifically comprises: determining the distance gate corresponding to the potential target point according to its position in the distance-Doppler matrix; for each data segment, extracting the complex signal value corresponding to the distance gate where the potential target point is located from the data segment; performing complex vector addition on all complex signal values in the data segment; calculating the square of the modulus of the complex vector addition result, and taking the calculation result as the coherent accumulation energy of the potential target point in the data segment.

4. The radar altimeter data processing method of claim 1, wherein, The discrimination and filtering of non-coherent interference target points according to the change rule of the coherent accumulation energy of the potential target points between data segments specifically comprises: calculating the coherent accumulation energy gain of the potential target point between the data segment with longer accumulation length and the data segment with shorter accumulation length; comparing the coherent accumulation energy gain with a preset threshold interval determined based on the coherent accumulation gain theory; if the coherent accumulation energy gain meets the requirement of the preset threshold interval, it is determined that the potential target point is a coherent signal of the same origin and is retained, otherwise it is determined as a non-coherent interference signal and is filtered out.

5. The radar altimeter data processing method of claim 4, wherein, The number of data segments is at least 3, and the accumulation lengths are in a multiple relationship; the discrimination and filtering step comprises: calculate coherence accumulation energy gain between the potential target point and each of the adjacent data segments with a multiple relationship in length, to obtain a plurality of coherence accumulation energy gains; count the number of coherence accumulation energy gains falling into a preset threshold interval; if the counted number reaches or exceeds a preset qualified number threshold, determine that the potential target point is a coherent signal and retain it; otherwise, determine that the potential target point is a non-coherent interference signal and filter it out.

6. The radar altimeter data processing method of claim 1, wherein, adopt a nearest neighbor algorithm to perform cross-frame point cluster on the Q target points to form Q target tracking chains, specifically including: establish and maintain a target tracking chain for each tracked target to record state information of the target in continuous multiple frames; predict a predicted state of each target tracking chain in the current frame according to state information of the target tracking chain in the previous frame; calculate an association cost between state information of the Q targets in the current frame and the predicted state of the Q target tracking chains; for each target tracking chain, find a target point with the minimum association cost with the predicted state from the Q targets in the current frame to associate; update state information of the successfully associated target point to the corresponding target tracking chain to form the target tracking chain in the current frame.

7. The radar altimeter data processing method according to any one of claims 1 to 6, characterized in that, The method further includes a target attention mechanism, and specific steps thereof include: monitor a signal-to-noise ratio of the target tracking chain corresponding to the final output value in real time; trigger the target attention mechanism when the signal-to-noise ratio is lower than a first preset threshold; if the target attention mechanism is triggered for continuous p frames, determine that the effective detection range of the radar altimeter has been exceeded; when it is determined that the effective detection range has been exceeded, set a confidence of the radar altimeter output height value to zero and keep outputting the effective height value in the last frame before the target attention mechanism is triggered.

8. The radar altimeter data processing method according to any one of claims 1 to 6, characterized in that, The method further includes an environment detection and determination step, specifically including: trigger environment detection when the target tracking chain corresponding to the final output value has a number mutation; calculate a root mean square average of energy of all FFT points in the distance dimension in the current frame; calculate a difference between the root mean square average and a historical background energy; if the difference exceeds a second preset threshold, determine that there is a real environment change and confirm and output a new height value; if the difference does not exceed the second preset threshold, determine that there is an error jump and reject the new height value; wherein the historical background energy is a statistical value of the root mean square average of the last L frames before the mutation occurs.

9. An electronic device comprising a memory, a processor, and a computer program or instructions stored on the memory, wherein the computer program or instructions, when executed by the processor, cause the electronic device to perform the method of any one of claims 1-8. The processor executes the computer program or instruction to implement the radar altimeter data processing method according to any one of claims 1-8.

10. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instruction is executed by the processor to implement the radar altimeter data processing method according to any one of claims 1-8.

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