The invention relates to the technical field of wireless target positioning, and in particular to a direct position determination (DPD) method for correcting a cubature Kalman filter. The method comprises the steps of: merging the angle-of-arrival (DOA) information in the received signals of a plurality of observation stations, and establishing a new cubature Kalman filter model by using a subspace data fusion method; based on the new cubature Kalman filter model, solving the objective function of the new cubature Kalman filter model by using an improved cubature Kalman filter algorithm to position a target. The method establishes an indirect observation model as the new cubature Kalman filter model to avoid the influence of transmitted signals and to fuse the positioning information of multiple observation stations; and by correcting a state covariance matrix and a observed noise covariance matrix, eliminates the noise introduced into the design of the cubature Kalman filter model; and has high parameter estimation efficiency and positioning efficiency.

Direct position determination method for correcting cubature Kalman filter

The invention belongs to the field of signal processing, provides a distributed network particle filter variable diffusion combination coefficient direct tracking and positioning method based on timedelay and Doppler, which aims to solve the problem that the tracking performance of a method for fixing a weight coefficient is deteriorated when the network signal to noise ratio environment is complex or severe. According to the invention, a local optimal cost function is constructed by using a local intermediate state estimation value; a constraint problem is converted into an unconstraint problem by applying a subspace projection method, an optimal solution is further obtained through an RLS iteration method to obtain a self-adaptive change combination coefficient, and compared with a traditional distributed particle filter target direct tracking method based on time delay and Doppler, a better tracking effect can be achieved. The method can be applied to the condition that the signal-to-noise ratio is bad, and the robustness and the robustness of the distributed self-adaptive particle filter direct tracking method are improved.

A distributed variable diffusion combination coefficient particle filter direct tracking method

The invention belongs to the signal processing field, and especially relates to a distributed adaptive tracking positioning method based on time difference and frequency difference in a passive positioning system; the method comprises the following steps: in a wireless sensor network with N receivers, local GPF randomly collects M random prediction particles from local important functions; received signals are exchanged between the receiver and the adjacent receiver, the receiver updates a weight value Wl matched with each prediction particle, and updates local intermediate state estimated values [psi] 1 and k and corresponding covariance P1 and k; [psi] 1 and k exchange is carried out between the receiver and the adjacent receiver, and the emitter position local estimated value x can be calculated; gauss distribution is constructed according to the emitter position local estimated value x and P1 and k, a condition probability density function is sampled so as to form the prediction particle of the next moment, and the receiver mean value and variance are calculated so as to build the approximation local prediction distribution according to a listed formula in description. The distributed positioning method can greatly improve the particle filter tracking positioning method performance.

Time delay and doppler based distributed adaptive particle filter direct tracking positioning method

The invention belongs to the field of signal processing, and provides a variable-step distributed self-adaptive parameter estimation method based on link noise. The method comprises the following steps: firstly, initializing each weight local estimation value as winit, and performing weight local estimation value exchange between each node and a neighbor bode in each neighborhood; and then computing intermediate estimation values psik and I by each node, and then self-adaptively updating the weight local estimation value wk and i+1 according to variable-steps muk and I by each node, and finally, iterating to convergence so as to obtain the weight local estimation value, namely, the estimation value of a filter coefficient. The link noise constrained variable-step is used for self-adaptively solving in a distributed way on each node, the link noise related information is sufficiently utilized, so that the parameter estimation effect is obviously improved; especially under a condition that the line noise is large, the distributed self-adaptive algorithm of the variable-step with the noise constraint overcomes the conflict of the algorithm convergence speed and the steady-state performance to a certain extent.

Variable-step distributed self-adaptive parameter estimation method based on link noise

The invention belongs to the technical field of signal processing, and provides a multi-target direct positioning method based on a self-adaptive clustering scheme, in order to solve the problem of multi-target positioning. The multi-target direct positioning method comprises the following steps: firstly, each receiver respectively receives a signal transmitted by a corresponding transmitter to obtain a discrete receiving signal xk[n] of a baseband, and exchanges the discrete receiving signal with a receiver belonging to the same basic unit group in a neighboring domain; secondly, the each receiver calculates an approximate value P [t, k] of intermediate values Psi [k, n] and P [t, k], exchanges the intermediate value Psi [k, n] with other receivers in the neighboring domain, and calculates a self-adaptive update combination coefficient b'[lk](n); and finally, each receiver calculates an estimated value P [k, n] of a target position. According to the multi-target direct positioning method disclosed by the invention, clustering of a receiver network is realized by means of the self-adaptive update combination coefficient, and therefore simultaneous positioning of a plurality of targets is realized.

Multi-target direct positioning method based on self-adaptive clustering scheme

The invention belongs to the field of signal processing, and provides a distributive self-adaption positioning method in a passive positioning system based on the time differenceThe distributive self-adaption positioning method aims at solving the contradiction between the convergence speed and the steady-state performance of an existing distributive self-adaption algorithm, and further improving the performance of the distributive self-adaption direct positioning methodThe method includes the steps that all receivers obtain dispersing receiving signals of a base band by collecting data, the noise power is estimated, first data exchange is carried out between the neighbor receivers, then the iteration updating core control parameter beta is set, the middle estimated value psi self-adaption operation is carried out, second data exchange between the neighbor receivers is carried out, the estimated value p of the position of a transmitter is iteratively estimated till the steady state is achieved, and the estimated value of the position of the transmitter is outputAccording to the distributive self-adaption positioning method, noise information is led, and positioning performance and tracking performance are remarkably improved; the contradiction between the positioning convergence speed and the steady-state performance can be overcome

Distributive self-adaption direct positioning method with noise constraint

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Time delay and doppler based distributed adaptive particle filter direct tracking positioning method

Variable-step distributed self-adaptive parameter estimation method based on link noise

Multi-target direct positioning method based on self-adaptive clustering scheme

Distributive self-adaption direct positioning method with noise constraint