Compressive Sampling Radar Target Parameter Estimation

In order to satisfy the requirements of modern radar high resolution, it is necessary to improve the signal bandwidth and increasing array aperture or increase the number of array elements. However, if still using the Nyquist theorem to sampling will cause the sample rate is too high and get a lot of sample data, and in this case the hardware circuit is also more complex. Using compression sampling theory to radar signal processing, can solve the problems of sampling, transmission, processing and storage of large amount of data.In this paper, study using compression sampling method to estimation the radar target parameters. The main work is as follows:1. The thesis studies the design of compressed sensing measurement matrix. We propose a chebyshev- Bernoulli measurement matrix construction algorithm. Through symbolic function map the sequence generated by chebyshev chaotic system to a sequence with elements only have ±1, and prove the sequence obey Bernoulli distribution, thus can construct of chebyshev-bernoulli measurement matrix from the sequence. Simulation results show that the measurement matrix can be achieved as well as chaotic and random matrices the same remodeling effect, also more stable than the random matrix, and can avoid the phenomena of intervaling sampling large amounts of data produced by the chaotic system that cause waste resources. Chebyshev- Bernoulli measurement matrix is easy to implement compared to other matrix, has a certain practical value, and more suitable for compressed sensing applications.2. On the basis of segmented Analog to Information Coverter proposed a partial segmented compression sampling structure. The equivalent measurement matrix of structure is transformed into the spare block diagonal matrix, and solve the problem of that the segmented AIC will bring pressure to hardware and storage because of equivalent measurement matrix is dense matrix. The structure of application in radar target parameter estimation to estimate the radar target distance-doppler information, thus can reduce the sampling rate and also improve resolution. The simulation results indicate that in case of having large compressive sampling data, partial segmented AIC and segmented AIC can achieve the same performance, but partial segmented AIC is more save hardware resources than segmented AIC.3. The thesis studies a space-time two-dimensional compression sampling array structure. The array of the received signal in angle domain sparsity is analyzed, and on the basis of the space mathematical model to design a space-time compression sampling array structure. Space compression structure is consist of chaos circuit and analog multiplier, can reduce the number of array elements receive channel and the complexity of hardware implementation. Time compression using partial segmented AIC structure, reduce the sampling rate while saving storage space. Simulation results show that the structure of space-time compressed samples can be accurately DOA estimation result at the same time reducing the number of array elements receive channels and the time domain sampling rate, indicating the feasibility of the structure.