Diagonal-loading robust adaptive radar beam forming method based on ridge parameter estimation

Abstract

The invention discloses a diagonal-loading robust adaptive radar beam forming method based on ridge parameter estimation. According to an implementation scheme, the method includes: receiving signals in real time by radar antennas; constructing adaptive weight vectors of a radar; defining output power of a radar antennal array; adopting a new estimation criterion for estimating unknown vectors; utilizing the estimated values of the unknown vectors for acquiring loading amount; and acquiring adaptive weight vectors for forming beams. The loading mount is determined in an adaptive diagonal loading method in the prior art by means of applying the new estimation criterion and a convex optimization tool. The method has the advantages that accuracy of weighted vectors of the radar antennas is improved, beams narrow in major lobe, low in minor lobe and capable of pointing to a target direction are formed, distortion of radar beams is avoided, desired signal receiving of the radar is enhanced, receiving of interference and noise is inhibited, errors in radar applications are resisted, radar targets can detected accurately, and false report or failure in report of targets by the radar is avoided.

Description

technical field [0001] The invention belongs to the technical field of array signal processing, and relates to radar signal beamforming technology, in particular to a diagonal loading robust adaptive radar beamforming method based on ridge parameter estimation, which is mainly used for radar target detection. Background technique [0002] Array signal processing is an important branch of modern signal processing, and its applications involve many technical fields such as radar, communication, biomedical engineering, and sonar. Beamforming, also known as airspace filtering, is used to detect targets in the radar field and is a major aspect of array processing. Its essence is to enhance radar target signals, The purpose of suppressing radar jamming signals. Adaptive beamforming can adaptively change the weighting factors of each array element of the radar according to the change of the signal environment. In the past 30 years, a large number of research results have emerged ...

AI Technical Summary

Problems solved by technology

Although this diagonal loading technique improves the distortion of the radar pattern to a certain extent, there are still the following problems: First, the effect of improving the distortion of the radar pattern is not obvious when the loading amount is too small; Second, when the loading amount is too large , will reduce the suppression strength of the radar to interference; 3. The loading amount selected according to the experience of the radar user is not only affected by human factors; The practical application of radar has great limitations. In order to improve the real-time search speed and accuracy of radar targets, a beamforming method that can fully adaptively adjust radar beams according to actual signal changes is needed.

However, the HKB method also has its shortcomings: the loading amount in the HKB method will increase with the increase of the number of snapshots. When the number of snapshots is large, the detection accuracy of the HKB method will decrease due to the excessive loading amount. cause the radar to miss the target or falsely report the target

However, it still has many shortcomings: First, the model it uses to re-estimate the covariance matrix of the radar received data is: the linear combination of the covariance matrix of the radar data and the identity matrix, that is, assuming that the noise received by the radar is Gaussian white noise, if The noise received by the actual radar is colored noise, and this method cannot accurately estimate the radar target; Second, in the process of solving the minimum mean square error of the covariance matrix, due to the lack of knowledge of the prior distribution information of the useful signal, the estimation operation There will be a large error, which will lead to a large error in the detected radar target; 3. When the number of snapshots is large, a small diagonal loading will be generated, and the GLC method will reduce the robustness against steering vector errors. That is, when the antenna deviates, the GLC method cannot accurately detect the target or even fail to detect the target

[0006] In summary, in the existing radar target detection schemes using beamforming, the SMI method and the LCMV method have the problems of radar beam distortion and the inability to fully adaptively detect radar targets according to changes in the environment; the HKB method has When the number of snapshots is slightly larger, the load is too large, and the radar detection performance decreases or fails; the GLC method is sensitive to prior information errors, and the snapshots lose their robustness against radar guidance vector errors after several hours.

These problems make the radar unable to detect the target accurately and quickly, and even fail to detect the target or falsely report the target

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