CN109031196A - Based on the direct localization method of maximum likelihood of the motion view survey station to multisignal source - Google Patents

Abstract

It is the invention belongs to radio signal source location estimation technical field, in particular to a kind of based on the direct localization method of maximum likelihood of the motion view survey station to multisignal source, include: each received multisignal source original observed data of motion view survey station in acquisition observation area；Using Doppler frequency between signal source and motion view survey station, receipt signal model is obtained；According to maximum-likelihood criterion, the cost function comprising all unknown parameters is obtained, and decoupling operation is carried out to it, the cost function after being optimized, which includes: transmitting signal, location information and multiple fading coefficients；Grid search is carried out to cost function, loop iteration obtains unknown parameter optimum combination solution and export to restraining.The present invention carries out abundant bottom fusion by combining each observation station original observed data, to data are received, and reduces location information loss, positioning accuracy is improved, the operand of multisignal source position positioning is reduced, performance is stable, reliable, and efficiently, there is stronger practical application value.

Description

A Maximum Likelihood Direct Location Method for Multiple Signal Sources Based on Moving Observation Stations

technical field

The invention belongs to the technical field of wireless signal source position estimation, in particular to a maximum likelihood direct positioning method for multiple signal sources based on a moving observation station.

Background technique

Traditional positioning methods include two separate steps: (1) Separate the intermediate parameters related to position information from the received signal, such as angle of arrival (AOA), time of arrival (TOA) and arrival time. Time difference of arrival (TDOA); (2) Use the parameters obtained in the first step to estimate the position. It is precisely because the traditional method contains two processing steps, which will introduce more errors. In addition, when multiple sources exist, the parameters separated in the first step may not be able to correctly match the transmitter, thus degrading the localization performance. The direct position determination (DPD) technology can directly obtain the position of the signal source from the received signal in a single processing, avoiding the information loss caused by multiple processing; and considering the internal relationship between each signal data. Therefore, the direct positioning method obtains higher positioning accuracy.

For the direct positioning method of single signal source, there are mainly subspace data fusion method and maximum likelihood method. The subspace data fusion method is also suitable for multi-source situations, using the orthogonality of the signal subspace and the noise subspace to achieve positioning with low computational complexity. But the robustness to low SNR is not strong, and the performance deteriorates at low SNR. The maximum likelihood method achieves higher positioning accuracy on the basis of greater computational overhead, especially at low signal-to-noise ratios. In addition, in the case of high-speed movement of the observation station, the effective use of Doppler information can increase the positioning accuracy. At present, the direct localization techniques for multiple signal sources mainly include subspace data fusion method, minimum variance undistorted response method and maximum likelihood algorithm. The first two methods have strong anti-interference and high resolution with low computational load, but they cannot be close to the corresponding Cramerot bounds, and the performance deteriorates severely in the case of low signal-to-noise ratio. The direct localization method based on the maximum likelihood criterion can be close to the Cramereau bound, and the localization performance is good under the condition of low signal-to-noise ratio. From this, the maximum likelihood iterative algorithm is derived, which greatly reduces the calculation amount of the traditional multi-source maximum likelihood method on the basis of sacrificing precision. The maximum likelihood direct positioning methods for multiple signal sources are mainly divided into traditional grid search methods and iterative algorithms. The traditional grid search method will face multi-parameter, high-dimensional nonlinear search problems, it is difficult to obtain closed-form solutions, the calculation amount is huge, and it is not time-sensitive. The iterative algorithm greatly reduces the calculation amount of the traditional grid search method through iterative solution, but most iterative algorithms cannot guarantee convergence or fall into local optimum, and cannot obtain the global optimal solution.

Contents of the invention

To this end, the present invention provides a maximum likelihood direct positioning method for multiple signal sources based on a moving observation station, using Doppler frequency and decoupling the cost function based on the maximum likelihood criterion, which not only improves the positioning accuracy, but also reduces the The amount of calculation is also improved, and the positioning performance is also improved under low signal-to-noise ratio.

According to the design scheme provided by the present invention, a maximum likelihood direct positioning method based on multiple signal sources of a motion observation station includes the following content:

A) Collect the original observation data of multiple signal sources received by each motion observation station in the observation area;

B) Using the Doppler frequency between the signal source and the moving observation station to obtain the received signal model;

C) Obtain a cost function including all unknown parameters according to the maximum likelihood criterion, and perform a decoupling operation on it to obtain an optimized cost function. The unknown parameters include at least: the transmitted signal, the position information of the signal and the complex fading coefficient; ;

D) Perform a grid search on the optimized cost function, and iterate until it converges, and obtain the optimal combination solution of unknown parameters and output it.

In the above, B), it is assumed that there are Q signal sources and L moving observation stations in the observation area, and each observation station is composed of M array elements to form a uniform linear array, and K observations are performed; within each observation time T , and the number of sampling points is N, then the received signal model obtained by the lth observation station at the kth observation is:

in, b _l,k,q is the channel attenuation between the qth signal source and the lth observation station in the kth observation, a _l,k (p _q ) represents the channel attenuation between the qth signal source and the lth observation station in the kth observation The direction vector between the l receiving stations, f _c is the known center frequency in the transmitted signal, μ _l,k (p _q ) is the distance between the qth signal source and the lth receiving station in the kth observation Doppler frequency, T _s represents the sampling time, s _k,q is the qth complex signal waveform in the kth observation, n _l,k is the Gaussian white noise received by the lth moving observation station in the kth observation .

The above, C) contains the following:

C1) Obtain a cost function for unknown parameters according to the maximum likelihood criterion;

C2) Decoupling the maximum likelihood estimation cost function, transforming the nonlinear multi-source problem into multiple single-source iterative problems.

Preferably, in C1), the unknown parameter of the qth signal source is expressed as: respectively represent the position of the qth signal source, the transmitted signal and the complex fading coefficient; all the unknown parameters of the Q signal source are expressed as: The maximum likelihood estimation cost function for the unknown parameter ξ is expressed as: in, b _l,k,q are the channel attenuation between the qth signal source and the lth observation station in the kth observation.

Preferably, in C2), define the vector In the i-th iteration, the maximum likelihood estimation cost function for η _q is expressed as in,

Preferably, in the decoupling operation of C2), the nonlinear multi-source problem is converted into multiple single-source iterative problems, specifically including the following:

First, for Q signal sources, obtain their channel attenuation estimates Then in the i-th iteration, the maximum likelihood estimation cost function of the unknown parameter η _q of the qth signal source is expressed as in,

Get an estimate of s _k,q :

followed by Substituting into the channel attenuation estimated value formula, we get Complete the update of the unknown parameter η _q of the qth signal source and all the unknown parameters ξ of the Q signal source until the maximum likelihood estimation cost function of the unknown parameters converges to the preset value, and stop the iteration.

Preferably, in D), the optimized cost function is carried out grid search, which specifically includes the following steps: first define the search range of the abscissa and ordinate; substitute each search point into the cost function; then, find the cost function amplitude The maximum value, the search point corresponding to this value is the estimated target position.

Beneficial effects of the present invention:

The present invention increases the utilization of Doppler information when the observation station is moving, reconstructs the signal model under multi-signal; decouples the cost function, and simplifies the multi-source problem into multiple single-source problems; by combining the observation stations The original observation data and the received data are fully fused at the bottom layer, which reduces the loss of position information and improves the positioning accuracy. By decoupling and optimizing the likelihood function, the complex nonlinear multi-source search problem is transformed into a low-complexity iteration. problem, so that it can effectively reduce the calculation amount of multi-signal source location positioning, and the performance is stable, reliable, and efficient, and has strong practical application value.

Description of drawings:

Fig. 1 is a schematic flow sheet of the present invention;

Fig. 2 is the root mean square error value graph of position estimation under different signal-to-noise ratios in the embodiment simulation experiment;

Fig. 3 is a graph of root mean square error value curves of position estimation under different snapshot numbers in the simulation experiment of the embodiment.

Detailed ways:

The present invention will be described in further detail below in conjunction with the accompanying drawings and technical solutions, and the implementation of the present invention will be described in detail through preferred embodiments, but the implementation of the present invention is not limited thereto.

For the location of multiple signal sources, the traditional grid search method has a large amount of calculation and is not time-sensitive. The iterative method reduces the amount of calculation, but cannot guarantee convergence or is limited to a local optimum, and cannot obtain a global optimal solution. For this reason, in the embodiment of the present invention, referring to Fig. 1, a maximum likelihood direct positioning method based on multiple signal sources of a motion observation station includes the following content:

A) Collect the original observation data of multiple signal sources received by each motion observation station in the observation area;

B) Using the Doppler frequency between the signal source and the moving observation station to obtain the received signal model;

C) Obtain a cost function including all unknown parameters according to the maximum likelihood criterion, and perform a decoupling operation on it to obtain an optimized cost function. The unknown parameters include at least: the transmitted signal, the position information of the signal and the complex fading coefficient;

D) Perform a grid search on the optimized cost function, and iterate until it converges, and obtain the optimal combination solution of unknown parameters and output it.

Use Doppler information for moving observation stations to improve positioning accuracy; decouple the maximum likelihood function of multiple targets, and iterate until convergence. Based on the maximum likelihood criterion, the maximum likelihood cost function is directly decoupled, the performance is improved under low signal-to-noise ratio, and the calculation amount of the network search method based on the maximum likelihood in the prior art is obviously reduced.

According to the Doppler frequency between each signal source and the moving observation station, in the process of obtaining the received signal model, another embodiment of the present invention assumes that there are Q signal sources and L moving observation stations in the observation area, each Observation stations are uniform linear arrays composed of M array elements and carry out K observations. Each array is strictly synchronized in time, then the received signal model obtained by the lth observation station at the kth observation is

Among them, 0≤t≤T, T is the observation time. In the kth observation interval, b _l,k,q and a _l,k (p _q ) are the channel attenuation and direction vector between the qth signal source and the lth receiving station respectively, s _k,q ( t) is the qth complex signal waveform, n _l,k (t) is the Gaussian white noise received by the lth receiving station, and finally, f _l,k,q are the qth signal source and the lth receiving station The signal frequency between , can be expressed as

where f _c is the known center frequency of the transmitted signal, μ _l,k (p _q ) is the Doppler frequency between the qth signal source and the lth receiving station, which can be expressed as:

where c is the signal propagation speed. Through down-conversion processing, the signal frequency can be approximated as

Within each observation time T, the number of sampling points is N. Therefore, the vector form of (1) can be expressed as

in,

The unknown parameter maximum likelihood estimation cost function optimization process under multiple signal sources. In another embodiment of the present invention, for the convenience of derivation, the unknown parameters of the qth signal source are expressed as

Therefore, all unknown parameters about the Q signal sources are

According to literature [7], the maximum likelihood estimation of ξ is obtained

in,

There are multiple unknown parameters in this function, requiring a high-dimensional search. Due to the large demand for computing resources, it is difficult to apply it to practical engineering. Moreover, this process consumes a lot of time, cannot guarantee the timeliness of the system, and causes a large positioning error. In order to solve this problem, the decoupling operation is performed on the cost function in the invention of this case. In this embodiment, by defining the vector

(5) can be reformulated as

in,

Therefore, in the ith iteration, the maximum likelihood estimate for η _q is

in,

So far, the decoupling of one transmitter from other transmitters has been completed, and the cost function has been optimized. Thus, only the unknown parameters related to the transmitter can be estimated in one process.

In yet another embodiment of the present invention, the maximum likelihood estimation cost function is decoupled through a two-step method. First, unknown parameters of Q signal sources are initialized. For Q signal sources, the estimated value of channel fading is obtained by minimizing (14)

Substituting (15) into (14) has

Because the first part in (16) has nothing to do with the qth signal source, it can be regarded as a constant. And then you can get

in,

According to matrix-related knowledge, the optimization problem in (18) can be expressed as

Note the Hermitian matrix and The eigenvalues are the same. Usually, the matrix The dimensionality of is significantly smaller than the matrix dimension. So (20) can be reformulated as

Compared with (20), the above formula obviously reduces the calculation amount. Through (18), get the estimated value of s _k,q

followed by Substituting (15) has

So far, the update of η _q is completed; similarly, continue to update ξ in this iteration until the cost function converges to a preset very small value, and then stop the iteration. In this embodiment, only the relevant parameters of one of the signal sources are estimated each time, and the relevant parameters of the remaining signal sources are regarded as constants, and the loop iterates to the preset convergence value, which reduces the amount of calculation and improves the performance of signal source position estimation under low signal-to-noise ratio.

Preferably, in D), the optimized cost function is carried out grid search, which specifically includes the following steps: first define the search range of the abscissa and ordinate; substitute each search point into the cost function; find the maximum value of the cost function , the search point corresponding to this value is the estimated target position.

In order to further verify the effectiveness of the present invention, the following will be further explained by specific simulation experiments:

The simulation experiment part compares the performance of the proposed algorithm (DML DPD), subspace data fusion algorithm (SDFDPD), two-step algorithm (TwoStep) and Cramereau bound (CRB) in the patent of this case. Considering that the initial positions of the three receiving stations are located at (3,0) ^T km, (10,3) ^T km and (7,8) ^T km respectively, and at the same time they are respectively at (300,0) ^T m/s, (0,300 ) ^T m/s and (-300,0) ^T m/s speed movement. To demonstrate its statistical performance, 500 Monte Carlo experiments are performed per scene.

Simulation 1: In order to verify the positioning performance of each algorithm, the root mean square error (RMSE) of the position estimation of each algorithm under different signal-to-noise ratios is given. It can be seen from Figure 2 that in low SNR, the algorithm (DML DPD) proposed in this case patent has the lowest root mean square error value, and the performance of the two-step algorithm is the worst. As the SNR increases, the DML DPD algorithm can quickly approach the corresponding Cramerot bound, while the other two algorithms cannot even approach the Cramerot bound when the SNR is 10dB. Therefore, the algorithm proposed in this patent case is superior to the other two algorithms and is more robust to low SNR.

Simulation 2: Also taking the first signal source as an example, continue to analyze the impact of the number of snapshots on the positioning performance of each algorithm. Set the signal-to-noise ratio to 0dB, the number of snapshots ranges from 50 to 350, and changes at equal intervals with a step size of 50, and examines the positioning performance of each algorithm. It can be seen from Figure 3 that the algorithm proposed in this case patent is still superior to the other two algorithms, and as the number of snapshots increases, it asymptotically approaches the corresponding Cramereau bound. It is worth noting that even when the number of snapshots is 350, the other two algorithms still cannot approach the corresponding Cramereau bound for the best localization performance.

Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the related information, please refer to the description of the method part.

The units and method steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, in the above description The composition and steps of each example have been generally described in terms of functions. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art may use different methods to implement the described functions for each particular application, but such implementation is not considered to exceed the scope of the present invention.

Those of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as: a read-only memory, a magnetic disk or an optical disk, and the like. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module/unit in the above embodiments can be implemented in the form of hardware, or can be implemented in the form of software function modules. The form is realized. The present invention is not limited to any specific combination of hardware and software.

The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A maximum likelihood direct positioning method for multiple signal sources based on a motion observation station is characterized by comprising the following steps:

A) acquiring original observation data of multiple signal sources received by each motion observation station in an observation area;

B) acquiring a received signal model by using the Doppler frequency between a signal source and a motion observation station;

C) according to the maximum likelihood criterion, obtaining a cost function containing all unknown parameters, and performing decoupling operation on the cost function to obtain an optimized cost function, wherein the unknown parameters at least comprise: transmitting signals, position information of the signals and complex fading coefficients;

D) and carrying out grid search on the optimized cost function, and circularly iterating until convergence, so as to obtain and output the optimal combination solution of the unknown parameters.

2. The method for the maximum likelihood direct positioning of multiple signal sources based on the motion observation station as claimed in claim 1, wherein in B), assuming that there are Q signal sources and L motion observation stations in the observation area, each observation station forms a uniform linear array by M array elements, and performs K times of observation; in each observation time T, the number of sampling points is N, and then the received signal model obtained by the ith observation station in the kth observation is:

wherein,b_l,k,qis the channel attenuation between the qth signal source and the l observation station in the kth observation, a_l,k(p_q) Representing the direction vector between the qth signal source and the l receiving station in the kth observation, f_cIs the known center frequency, mu, of the transmitted signal_l,k(p_q) Is the Doppler frequency, T, between the qth signal source and the l receiving station in the kth observation_sRepresenting the sampling time, s_k,qIs the q-th complex signal waveform in the k-th observation, n_l,kIs the white gaussian noise received by the ith mobile observation station in the kth observation.

3. The method for maximum likelihood direct localization of multiple signal sources based on a motion-based observatory of claim 2, wherein C) comprises the following:

C1) obtaining a cost function aiming at unknown parameters according to a maximum likelihood criterion;

C2) and (4) performing decoupling operation on the maximum likelihood estimation cost function, and converting the nonlinear multi-source problem into a plurality of single-source iteration problems.

4. The method of claim 3, wherein in C1), the unknown parameters of the qth signal source are expressed as: respectively representing the position, the transmitting signal and the complex fading coefficient of the q signal source; all unknown parameters of the Q signal sources are expressed as:the maximum likelihood estimation cost function for the unknown parameter ξ is represented as:wherein,

5. the method as claimed in claim 4, wherein in C2), a vector is definedin the ith iteration, on η_qIs expressed as a maximum likelihood estimation cost function ofWherein,

6. the method for maximum likelihood direct localization of multiple signal sources based on a moving observation station according to claim 4, wherein the decoupling operation of C2) transforms a nonlinear multiple source problem into multiple single source iteration problems, which specifically includes the following steps: firstly, for Q signal sources, obtaining the channel attenuation estimated valuethen in the ith iteration, the qth signal source unknown parameter η_qIs expressed as a maximum likelihood estimation cost function ofWherein, to obtain s_k,qEstimated value of (a):then, willSubstituting into the channel attenuation estimation value formula to obtaincompleting unknown parameters η for the qth signal source_qand updating all unknown parameters ξ of the Q signal sources until the maximum likelihood estimation cost function of the unknown parameters converges to a preset value, and stopping iteration.

7. The method for maximum likelihood direct localization of multiple signal sources based on a moving observation station according to claim 1, wherein the grid search of the optimized cost function in D) specifically comprises the following steps: firstly, defining a search range of a horizontal coordinate and a vertical coordinate; substituting each search point into a cost function; then, the maximum value of the amplitude of the cost function is searched, and the search point corresponding to the maximum value is the estimated target position.