A method to improve the detection probability of weak targets

Abstract

The present invention relates to the field of radar signal processing, and in particular to a method for improving the detection probability of a weak target. The present invention uses frequency domain pulse compression without windowing to retain the energy of the first side lobe, adopts the addition of the main lobe and the first side lobe energy to improve the signal-to-noise ratio of the weak target, then performs a sliding window search for the position of the target in the distance dimension, uses a lower first threshold for detection, and finally performs binary accumulation between pulses, uses the detection results of multiple pulses to reduce the false alarm probability, and improves the detection probability of the weak target.

Description

Technical Field

[0001] The invention relates to the field of radar signal processing, and in particular to a method for improving the probability of weak target detection. Background Art

[0002] With the deterioration of battlefield electromagnetic environment and the development of electronic countermeasure technology, the development and design of modern radar systems must take into account the means of anti-reconnaissance and anti-electronic interference. Common electronic interference includes active interference and passive interference. Passive interference is to interfere with radar detection of targets by artificially spreading a large number of foil strips to form a strong reflection signal. Active interference is generally implemented in two ways. The first way is to interfere with the radar's detection of targets by releasing powerful noise to interfere with the radar's receiver. The other way is to interfere with radar detection of targets by radiating false radar echoes.

[0003] Random frequency agility between pulses is an important means for radar to counter active interference, and has a good anti-electronic countermeasure effect. However, most radars currently destroy the phase coherence of adjacent echo signals when they are in inter-pulse frequency agility, which makes it difficult to effectively coherently accumulate the energy of the echo signals within the accumulation period, causing the target echo amplitude to be high and low. In the tracking process, the tracking target may be lost, which seriously affects the radar target tracking and detection performance. The use of non-coherent accumulation can basically solve this problem, but the signal-to-noise ratio will be reduced. The traditional non-coherent accumulation method is to calculate the modulus value of the echo signal and then sum the results of multiple pulses, which will cause the clutter, noise and interference in the echo signal to be superimposed, resulting in a decrease in target detection capability, especially weak targets will be submerged in the noise, and the probability of false alarm is increased, which is a significant disadvantage of the traditional method. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a target detection method based on a combination of sliding window search and binary accumulation judgment. The method of the present invention includes: a distance dimension sliding window search and detection method, and an inter-pulse binary accumulation judgment method. The present invention reduces the signal-to-noise ratio required for detecting the target and improves the weak target detection capability under the condition that the false alarm probability remains unchanged. The present invention also has the advantages of versatility, high computational efficiency, and easy implementation in engineering, and has been applied to phased array radars and has been successfully verified.

[0005] To solve the above technical problems, the technical solution of the present invention is a method for improving the probability of weak target detection, comprising the following steps:

[0006] Step 1: Collect the echo signal of a pulse repetition period;

[0007] Step 2: Perform frequency domain pulse compression processing on the echo signal, using the pulse compression coefficient without windowing;

[0008] The beneficial effect of this step is: using the pulse compression coefficient without windowing, retaining the energy of the first sidelobe, and only using the main lobe energy of the weak target and the main lobe energy of the noise and clutter for detection. The signal-to-noise ratio of the weak target is not high, and the detection probability is greatly reduced. Therefore, adding the main lobe and the first sidelobe energy is equivalent to improving the signal-to-noise ratio of the weak target, which is beneficial to improving the detection probability.

[0009] Step 3: Take the modulus and logarithm of the echo signal after pulse compression, calculate the average of the clean area data of the echo vector, and obtain the detection noise floor;

[0010] The beneficial effect of this step is that the clean area data is used to find the detection noise floor, which avoids the influence of clutter, can accurately obtain the noise estimation of the current echo, and use this noise floor for detection in subsequent steps.

[0011] Step 4: Perform a range-dimensional sliding window search on the echo vector, calculate the mean of 7 range units, set the search window size to 7 range units, and set the sliding window interval size to 3 range units;

[0012] The beneficial effects of this step are as follows: the sliding window searches for the position of the target in the distance dimension, and 7 distance units are selected to include the positions of the main lobe and the first side lobe, accumulating the energy of the main lobe and the side lobe, so that the energy of the target is stronger, not only has better detection performance for weak targets, but also makes it easier to distinguish between targets and clutter. The sliding window interval is 3 distance units to avoid the accumulation results of two adjacent sliding windows being similar, and the target position cannot be determined;

[0013] Step 5: Detect the accumulated echo vector according to the first threshold and the noise floor calculated in step 3;

[0014] The beneficial effects of this step are: using a lower threshold allows weaker signals to pass detection, reducing the probability of missed detection;

[0015] Step 6: Repeat steps 1 to 5 to obtain detection results of multiple pulse repetition periods within the wave position, wherein the carrier frequency of each pulse repetition period is different;

[0016] Step 7: For the detection result of step 6, according to the criterion of N / K ≥ 0.5, use the second threshold to perform pulse-to-pulse binary accumulation, K is the number of frames of data, that is, the number of pulses, and N is the value of binary accumulation.

[0017] The beneficial effect of this step is: using inter-pulse binary accumulation to perform secondary detection on the result of step 6. Since the first threshold set in step 6 is relatively low in order to detect weaker targets, there are more false alarms. The echo intensity of clutter and noise is random between multiple pulses. This feature is used to reduce the probability of false alarms by using the detection results of multiple pulses. Compared with traditional non-coherent accumulation, while ensuring the false alarm probability, it not only improves the probability of weak target detection, but also reduces the signal-to-noise ratio required for weak target detection.

[0018] The beneficial effects of the present invention are as follows: when performing frequency domain pulse compression, a pulse compression coefficient without windowing is used, the energy of the first side lobe is retained, and the energy of the main lobe and the first side lobe is added, which is equivalent to improving the signal-to-noise ratio of the weak target, which is beneficial to improving the detection probability; then the clean area data is used to find the detection noise floor, avoiding the influence of clutter, and accurately obtaining the noise estimation of the current echo; secondly, a sliding window is performed on the distance dimension to search the position of the target, and the energy of the main lobe and the side lobe is accumulated, so that the energy of the target is stronger, not only has better detection performance for weak targets, but also makes it easier to distinguish between targets and clutter, and then a lower threshold is used to make weaker signals pass the detection, reducing the probability of missed detection; finally, inter-pulse binary accumulation is used for secondary detection, and the detection result of multiple pulses is used to reduce the false alarm probability. Compared with traditional non-coherent accumulation, under the premise of ensuring the false alarm probability, not only the detection probability of weak targets is improved, but also the signal-to-noise ratio required for weak target detection is reduced. The method of improving the detection probability of weak targets by non-coherent accumulation of the present invention can not only ensure the maximization of the radar anti-electronic countermeasures performance, but also keep the detection performance of the radar itself from being reduced as much as possible due to frequency agility. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A method for improving the probability of weak target detection, flow chart of a specific embodiment

[0020] Figure 2 Comparison diagram between the traditional non-coherent accumulation method and the method of the present invention

[0021] Glossary

[0022] FFT: Fourier transform. IFFT: Inverse Fourier transform. DETAILED DESCRIPTION

[0023] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0024] The embodiment of the present invention provides a method for improving the probability of weak target detection, which can be applied to the field of radar signal processing.

[0025] like Figure 1 As shown, a method for improving the probability of weak target detection includes the following steps:

[0026] Step 1: The echo signal of one pulse repetition period is represented by the vector X(t) = [x1, x2, …, x M ] means, where x1,x2,…,x M is the echo value of M distance units, generally M is between 1000 and 8000, preferably between 4000 and 6000, in this embodiment, M=5000;

[0027] Step 2: Perform frequency domain pulse compression processing on the echo signal, using the pulse compression coefficient without windowing. The purpose of this step is to retain the energy of the first sidelobe, and only use the main lobe energy of the weak target and the main lobe energy of the noise and clutter for detection. The signal-to-noise ratio of the weak target is not high, and the detection probability is greatly reduced. Therefore, the addition of the main lobe and the first sidelobe energy is equivalent to improving the signal-to-noise ratio of the weak target, which is beneficial to improving the detection probability. Suppose the transmitted signal is S(t), its spectrum is S(ω)=FFT(S(t)), and the spectrum of the echo signal is X(ω)=FFT(X(t)). The formula for frequency domain digital pulse compression without windowing is: Y(t)=IFFT[X(ω)·S * (ω)];

[0028] Step 3: Take the logarithm of the echo signal after pulse compression to obtain the vector Y′(t)=[y′1,y′2,…,y′ M ], calculate the average of the clean area data of the echo vector, that is, take the data at the end of the pulse repetition period to obtain the detection noise floor In this implementation, h=2000 is taken, the first 3000 distance units are removed, and the last 2000 distance units are taken to calculate the detection to the end, that is, The purpose of this step is to use the clean area data to find the detection noise floor, avoid the influence of clutter, accurately obtain the noise estimation of the current echo, and use this noise floor for the detection in the subsequent steps;

[0029] Step 4: For the echo vector Y′(t)=[y′1,y′2,…,y′ M ] to perform distance dimension sliding window search, calculate the mean of 7 distance units, and obtain the accumulated echo vector Z(t)=[z1,z2,…,z M-3 ],in Where i=1,2,3,...,M-3, the search window size is 7 distance units, and the sliding window interval size is 3 distance units. The purpose of this step is to perform sliding window search on the distance dimension. The 7 distance units are selected to include the positions of the main lobe and the first side lobe, and accumulate the energy of the main lobe and the side lobe. In this way, the energy of the target is stronger, which not only has better detection performance for weak targets, but also makes it easier to distinguish between targets and clutter. The sliding window interval is 3 distance units to avoid the accumulation results of two adjacent sliding windows being similar and unable to determine the target position. The search window is 7 distance units and the sliding window is 3 distance units, which is optimal in terms of calculation amount and effect.

[0030] Step 5: Accumulate the echo vector Z(t) = [z1, z2, ..., z M-3 ], according to the threshold V T1 And the noise floor n(t) calculated in step 3 is detected to obtain the detection result vector W(t) = [w1,w2,…,w M-3 ], take V T1 =4dB, the test results are represented by 0 and 1, that is, z i -n(t)<V T1 , w i =0 means it does not pass the threshold; z i -n(t)≥V T1 , w i = 1 represents the threshold is exceeded, where i = 1, 2, 3, ..., M-3. The purpose of this step is to use a lower threshold V T1 , generally V T1 ≤7dB, so that weaker signals can also pass the detection, reducing the probability of missed detection;

[0031] Step 6: Repeat steps 1 to 5 to obtain the detection results of multiple pulse repetition periods in the wave position W = [W(1), W(2), ..., W(K)] T , K represents all pulse repetition periods in one wave position, such as pulse repetition period 8, then K = 8, where the radar works in pulse frequency agility mode, and the carrier frequency of each pulse repetition period is different;

[0032] Step 7: For the detection results of step 6, perform pulse-to-pulse binary accumulation according to the criterion of N / K ≥ 0.5, where K is the number of data frames, i.e. the number of pulses, and N is the value of binary accumulation. N is less than or equal to K, and N / K ≥ 5 / 8, i.e. the threshold V T2 =5, that is, if there is a target in 5 frames among the 8 consecutive frames of data detected in step 6, it is considered to be a target. The purpose of this step is to use the pulse-to-pulse binary accumulation to perform a secondary detection on the result of step 6. Since the threshold V is set in step 6 to detect weaker targets, T1The probability of false alarm is relatively low, and the echo intensity of clutter and noise is random between multiple pulses. This feature is used to reduce the probability of false alarm by using the detection results of multiple pulses. Compared with the traditional non-coherent accumulation, under the premise of ensuring the false alarm probability, it not only improves the probability of weak target detection, but also reduces the signal-to-noise ratio required for weak target detection.

[0033] Figure 2 The experiment shows that when the threshold is 4dB, the same false alarm probability P fa =10 -6 Under this condition, the detection probability of the present invention is P d =0.8, while the detection probability of the traditional non-coherent accumulation method is only P d =0.5. Simulation experiments show that the signal-to-noise ratio required for target detection can be reduced while keeping the false alarm probability unchanged, thus improving the ability to detect weak targets.

[0034] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for improving the probability of weak target detection, characterized in that: The following steps are involved: Step 1: Collect the echo signal of a pulse repetition period; Step 2: Perform frequency domain pulse compression processing on the echo signal, using the pulse compression coefficient Y(t) without windowing; Step 3: Take the logarithm of the echo signal after pulse compression to obtain the vector Y′(t), calculate the average of the clean area data of the echo vector, and obtain the detection noise floor Step 4: Perform a range-dimensional sliding window search on the echo vector, calculate the mean of 7 range units, set the search window size to 7 range units, and set the sliding window interval size to 3 range units; Step 5: Detect the accumulated echo vector according to the first threshold and the noise floor calculated in step 3. Generally, the first threshold should be less than or equal to 7dB. Step 6: Repeat steps 1 to 5 to obtain detection results of multiple pulse repetition periods within the wave position, wherein the carrier frequency of each pulse repetition period is different; Step 7: For the detection result of step 6, use the second threshold to perform pulse-to-pulse binary accumulation.

2. The method for improving the probability of weak target detection according to claim 1, characterized in that: The pulse compression coefficient without windowing is Y(t) = IFFT[X(ω)·S * (ω)], X(ω) is the spectrum of the echo signal, and S(ω) is the spectrum of the transmitted signal.

3. The method for improving the probability of weak target detection according to claim 1, characterized in that: The first threshold is 4dB.

4. The method for improving the probability of weak target detection according to claim 1, characterized in that: The second threshold is set according to the principle of N / K≥0.5, where K is the number of data frames, N is the value of binary accumulation, and N is less than or equal to K.

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