A joint estimation method of target angle distance based on sparse representation

Abstract

The invention discloses a target angle and distance combined estimation method based on sparse representation. The target angle and distance combined estimation method based on sparse representation mainly resolves the problem that the calculation amount is large in the prior art. According to the technical scheme, the target angle and distance combined estimation method based on sparse representation comprises the steps that (1) discretization is carried out on received signals of elements, and a received signal matrix is constructed; (2) the angle range and the distance angle of a detected area are respectively divided at equal intervals, and a sparse base based on the angle and a sparse base based on the distance are constructed; (3) a sparse optimization module is built according to the received signal matrix, the sparse base based on the angle and the sparse base based on the distance and is solved through a two-dimensional alternating optimization method to obtain a final optimization solution; (4) the angle and the distance of a target are obtained through peak value searching on the final optimization solution. The target angle and distance combined estimation method based on sparse representation has the advantages of being small in calculation amount and accurate in estimation, and can be applied to target detection in radar and sonar.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a target estimation method, which can be used for target detection in radar and sonar. Background technique [0002] The phased array is an array antenna that uses the coherence principle of electromagnetic waves to control the phase of the current fed to each radiating array element through a computer, thereby changing the beam direction. Phased array adopts electronic scanning method, which can realize real-time update of echo data, so it has gained wide attention. Among them, using phased array to estimate target position is a main aspect of phased array application. [0003] At present, there are many methods for estimating the target position, such as the multiple signal classification MUSIC method, the ESPRIT method for estimating signal parameters using rotation-invariant techniques, and methods based on sparse representation, among which: [0004] ...

AI Technical Summary

Problems solved by technology

For example, Wang Peng's master thesis "Joint Estimation of Target Azimuth and Distance and High-resolution Imaging of Array Antenna Radar" is a method based on sparse representation. The biggest shortcoming of this method is that in the sparse optimization model, the angular distance of the target is double The parameters are constructed in a sparse domain. If the grid is too dense, the dimension of the sparse domain will be huge, which will make the calculation of the optimization solution very large, and the engineering application will be difficult.

Images