The present invention discloses an estimation method of a quasi-stationary broadband array signal direction of arrival (DOA) based on block sparse Bayesian learning. An intra-frame correlation and aninterframe independence of a quasi-stationary broadband signal frequency spectrum are employed to set a corresponding a block sparse prior distribution model for signals, and a block sparse Bayesian model is employed to perform estimation of sparse signals, so that an estimation result with higher precision is obtained. Array receiving signals are subjected to appropriate framing processing, eachframe of the signal is subjected to Fourier transform, and each block sparse Bayesian model for each signal is established in a frequency domain; under an assumption of each frame of the signal is independent, information of all the frames is combined to establish a total Bayesian model, and hyper-parameter vectors are employed to control a sparsity of all the frames of signals to be reconstructed; and finally, the expectation maximization algorithm (EM) is employed to obtain an iterative update formula of the hyper-parameter vectors. The estimation method of a quasi-stationary broadband arraysignal direction of arrival based on block sparse Bayesian learning fully utilizes a short-time stability feature of quasi-stationary broadband array signals to establish a block sparse model, and therefore a higher DOA estimation precision can be obtained.

Estimation method of quasi-stationary broadband array signal direction of arrival based on block sparse Bayesian learning

The invention discloses a distorted towed array line spectrum feature enhancement method and system The method comprises the following steps: 1, acquiring an observation array signal; 2, roughly estimating a target orientation based on ideal beam forming; 3, detecting L frequencies with the maximum power of the target signal, and calculating a relative delay inequality vector gamma l of the strong line spectrum array; 4, fitting the L strong line spectrum array relative time delay difference vectors gamma l into L curves; 5, classifying the L curves by using a Frechet distance-based hierarchical clustering algorithm to obtain a classification cluster set; 6, selecting a class with most line spectrums in a classification result, and obtaining array relative delay inequality vector estimation gamma' for eliminating the influence of strong interference; 7, obtaining a target tracking beam based on the estimated time delay, and obtaining fidelity-enhanced line spectrum features through Fourier transform According to the method, the influence of a strong interference line spectrum on time delay estimation is eliminated through fitting-hierarchical clustering, a target radiation noisetracking beam is obtained based on the estimated time delay, and line spectrum feature enhancement based on a distorted towed array is realized in a multi-target strong interference underwater acoustic environment

Distorted towed array line spectrum feature enhancement method and system

The invention provides a radiation source direct positioning method based on a block sparse Bayesian model, and belongs to the technical field of radiation source positioning. The radiation source direct positioning method comprises the steps: block sparse modeling of received data, posterior updating of signal statistical parameters, and model parameter resolving. The method provided by the invention is suitable for the condition that a radiation source signal has a narrowband characteristic for each base station and has a broadband characteristic between the base stations, and only the arrival angle information of the signal is utilized in the processing process, and no receiving synchronization requirement is required for each base station. The method has the advantages of the block sparse Bayesian class method: the number of targets does not need to be known, manual setting of hyper-parameters is not needed, and the positioning precision is better than that of a conventional subspace class method.

Radiation source direct positioning method based on block sparse Bayesian model

The present invention provides a target orientation tracking method based on the Bayesian method, belonging to the array signal processing field. The problems are solved that the DOA (direction of arrival) tracking method based on spatial category is poor in precision and known conditions required by the particle filter DOA tracking method are too many in the condition of a low signal to noise ratio. Through adoption of the Taylor expansion formula, the target orientation tracking method based on the Bayesian method allows the DOA tracking in an angle slow varying mode to be modeled to a dynamic model, converts the angle tracking to the parameter estimation problem in a probability model based on the Bayesian theory, and performs correction of the information of the previous moment through adoption of the EM algorithm according to the signal arrival angle, the signal power and the estimation value of the noise power at the previous moment so as to realize the tracking of the signal arrival angle. The target orientation tracking method based on Bayesian method is mainly used for tracking of the target orientation.

Target orientation tracking method based on Bayesian method

Generally, particle filter is used in the single snapshot situation and the initial state is assumed to be known. To make the measurement interval be small enough, we construct a multiple measurement vectors model for DOA tracking since it usually outperforms the single measurement vector model. And we take the initial state into consideration. The initial tracking error of the particle filter becomes very large when the initial state is unknown. Thus, we modify the initial state according to the likelihood of the generated random samples. The method is numerically evaluated using a uniform linear array in simulations. The results show that the proposed algorithm has higher tracking accuracy.

Particle Filter with Correction of Initial State for Direction of Arrival Tracking

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Target orientation tracking method based on Bayesian method

Particle Filter with Correction of Initial State for Direction of Arrival Tracking