CN116400318B - Multi-observation target position estimation method and device based on online particle swarm optimization - Google Patents
Multi-observation target position estimation method and device based on online particle swarm optimization Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G01S13/06—Systems determining position data of a target
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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Abstract
The application relates to a multi-observation target position estimation method and device based on online particle swarm optimization, a multi-observation likelihood function of a target is constructed through multipath echo signals, an online particle swarm optimization algorithm is adopted to carry out iterative solution on a maximum likelihood estimation value of the multi-observation likelihood function, in each iterative process, multipath echo parameters are calculated according to the current positions of particles through online ray tracking, then the current likelihood function value is obtained through calculation, meanwhile, the historical optimal positions of the particles and the particle swarm are updated according to the function value, the convergence degree is calculated, if the convergence degree meets the convergence condition, the maximum likelihood estimation value is obtained to stop calculation, if the convergence degree does not meet the convergence condition, the cognition factor of the particle swarm optimization algorithm is updated according to the convergence degree in a self-adaption mode, meanwhile, the speed and the position of the particles are updated, and iterative calculation is continued until convergence. The method can improve the accuracy of position estimation of the target in the complex heterogeneous multipath scene.
Description
Technical Field
The present disclosure relates to the field of radar signal processing technologies, and in particular, to a method and an apparatus for estimating a position of a multi-observation target based on online particle swarm optimization.
Background
The low-altitude target detection technology in urban background is mainly divided into two major categories, namely a photoelectric passive detection technology such as visible light and infrared and a radar active detection technology, wherein the radar has the characteristics of all weather and time, the detection performance is little influenced by time and weather, the detection range is large, the action distance is long, and the method has important significance to the urban area monitoring field. However, the dense buildings in urban environments can cause unavoidable target shielding phenomenon and complex multipath effects, and provide serious challenges for radar low-altitude target detection.
The radar signal can generate multipath effects such as reflection, diffraction and the like in urban environment, and false targets can be generated by the radar. In practice, the multipath echo is superposition of multiple echo components of different propagation paths, each echo path is determined by the radar position, the target position and the scene structure, and all the echo paths carry partial information of the target, so that each path is equivalent to one-time observation of the target, the multipath effect is converted into a multiple-observation problem of the target, and the performance of radar low-altitude target detection can be effectively improved.
After the radar receives multipath echo signals of the target, through modeling a multi-observation target likelihood function, the estimation of the unknown position of the target can be obtained through maximum likelihood estimation, but the maximum likelihood estimation does not have a closed-form analytic solution in a general form. The existing method is used for constructing a specific likelihood function to solve maximum likelihood estimation aiming at a specific multipath scene, and the problem of solving the maximum likelihood of a target in a complex heterogeneous scene is difficult to solve due to high dependence on scene geometric priori.
Disclosure of Invention
In view of the above, it is desirable to provide a method and apparatus for estimating a multi-observation target position based on online particle swarm optimization, which can realize high-precision target position estimation.
A multi-observation target position estimation method based on online particle swarm optimization, the method comprising:
acquiring a multipath echo signal of a target;
constructing a multi-observation likelihood function of the target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
in each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the previous iteration, namely the current particle position, calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
Performing convergence degree calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
if the calculation result does not meet the preset convergence condition, adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result, simultaneously updating the speed and the position of each particle, and performing the next iterative calculation until the convergence calculation result meets the preset convergence condition;
and obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
In one embodiment, the geometric prior information of the scene where the target is located is obtained, and when the on-line particle swarm optimization algorithm is adopted to carry out iterative solution, each particle position in the particle swarm is initialized according to the geometric prior information of the scene.
In one embodiment, the convergence is expressed as:
；
wherein ,；
in the above-mentioned description of the invention,representing the current position set of each particle,>represents the historic optimal position of the center particle, i.e. particle swarm, < >>Representation->At->Is->Particles contained in the neighborhood->Representing the potential of the population of particles.
In one embodiment, the following formula is adopted for adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result:
；
；
in the above-mentioned description of the invention,representing the convergence calculation result, +.>Representing individual cognitive factors->Representing social group cognitive factors.
In one embodiment, after adaptively updating the cognitive factors of the particle swarm optimization algorithm according to the convergence calculation result, updating the speed and the position of each particle includes:
calculating according to the positions of the particles obtained in the previous iteration, namely the positions of the current particles and the moving speed of the particles in the previous iteration, and obtaining the moving speed of the corresponding particles in the current iteration;
and calculating according to the moving speed of each particle in the current iteration, and correspondingly obtaining the moved position of each particle, namely the position of each particle in the next iteration.
In one embodiment, the calculation is performed according to the position of each particle obtained in the previous iteration, that is, the position of each current particle, and the moving speed of each particle in the previous iteration, so as to obtain the moving speed of the corresponding particle in the current iteration, where the moving speed is calculated by the following formula:
；
in the above-mentioned description of the invention,represents the number of iterations, +. >Indicate->The individual particles are at->Wheel speed->The inertia factor is represented by a factor of inertia,/>indicate->The individual particles are at->Wheel position-> and />Respectively representing the individual cognitive factors and the social group cognitive factors,/for each individual> and />Respectively express to->Iteration up to->Historical optimal position of individual particles and particle groups, < >> and />The individual awareness weight and the social awareness weight are represented, respectively.
In one embodiment, the calculation is performed according to the moving speed of each particle during the current iteration, so as to correspondingly obtain the moved position of each particle, that is, the position of each particle during the next iteration adopts the following formula:
。
a multi-observation target position estimation apparatus based on online particle swarm optimization, the apparatus comprising:
the multipath echo signal acquisition module is used for acquiring multipath echo signals of the target;
the multi-observation likelihood function construction module is used for constructing a multi-observation likelihood function of the target according to the multipath echo signals and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
the first iterative computation module is used for calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the last iteration, namely the current particle position, and calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
The second iterative calculation module is used for carrying out convergence calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets the preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
the third iterative computation module is used for adaptively updating the cognitive factors of the particle swarm optimization algorithm according to the convergence degree computation result if the computation result does not meet the preset convergence condition, updating the speed and the position of each particle at the same time, and performing the next iterative computation until the convergence degree computation result meets the preset convergence condition;
and the target position estimation module is used for obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring a multipath echo signal of a target;
constructing a multi-observation likelihood function of the target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
In each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the previous iteration, namely the current particle position, calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
performing convergence degree calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
if the calculation result does not meet the preset convergence condition, adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result, simultaneously updating the speed and the position of each particle, and performing the next iterative calculation until the convergence calculation result meets the preset convergence condition;
and obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring a multipath echo signal of a target;
constructing a multi-observation likelihood function of the target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
in each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the previous iteration, namely the current particle position, calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
performing convergence degree calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
if the calculation result does not meet the preset convergence condition, adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result, simultaneously updating the speed and the position of each particle, and performing the next iterative calculation until the convergence calculation result meets the preset convergence condition;
And obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
According to the multi-observation target position estimation method and device based on online particle swarm optimization, the multi-observation likelihood function of the target is built according to the acquired multi-path echo signals, the maximum likelihood estimation value of the multi-observation likelihood function is subjected to iterative solution by adopting an online particle swarm optimization algorithm, in each iterative process, the multi-path echo parameters are calculated according to the current positions of all particles by online ray tracking, the multi-observation likelihood function is calculated by utilizing the parameters to obtain the current likelihood function value, meanwhile, the historical optimal positions of all particles and the particle swarm are updated according to the function value, then the convergence degree of the iteration is calculated, if the convergence degree meets the convergence condition, the value of the currently obtained multi-observation likelihood function is the maximum likelihood estimation value, calculation is stopped, if the convergence degree does not meet the convergence condition, the cognition factor of the particle swarm optimization algorithm is adaptively updated according to the calculated convergence degree, and the speed and the position of all particles are updated at the same time, and the next iterative calculation is carried out until convergence is achieved. The method is applicable to target position estimation in complex heterogeneous multipath scenes, and the convergence criterion robustness in the linear particle swarm optimization algorithm after optimization in the method is high.
Drawings
FIG. 1 is a flow chart of a multi-observation target position estimation method based on online particle swarm optimization in an embodiment;
FIG. 2 is a schematic top view of an urban multipath scene in a simulation experiment;
FIG. 3 is a schematic diagram of simulation results of random initial states of particle swarms in a simulation experiment;
FIG. 4 is a schematic diagram showing simulation results of the 24 th iteration of the particle swarm in a simulation experiment
FIG. 5 is a schematic diagram showing simulation results of the 45 th iteration of the particle swarm in a simulation experiment
FIG. 6 is a schematic diagram showing the result of the simulation of the termination of the 80 th iteration algorithm of the particle swarm in a simulation experiment
FIG. 7 is a schematic diagram showing the particle swarm convergence with iteration number based on PSO algorithm in a simulation experiment
FIG. 8 is a schematic diagram of a variation curve of particle swarm convergence of a PSO algorithm adjusted by adaptive cognitive factors with iteration times in a simulation experiment;
FIG. 9 is a graph showing the maximum likelihood function value as a function of the number of iterations in a simulation experiment;
FIG. 10 is a graph showing the variation of the estimation error of the target position with the number of iterations in a simulation experiment;
FIG. 11 is a flow chart of a multi-observation target position estimation method based on online particle swarm optimization in another embodiment
FIG. 12 is a block diagram of a multi-observation target position estimation apparatus based on online particle swarm optimization in one embodiment;
fig. 13 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Aiming at the problem of radar target position estimation in a complex multipath scene of a city, as shown in fig. 1, the multi-observation target position estimation method based on online particle swarm optimization is provided, and comprises the following steps:
step S100, acquiring a multipath echo signal of a target;
step S110, constructing a multi-observation likelihood function of a target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
step S120, in each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the current particle position which is the particle position obtained in the previous iteration, calculating multiple observation likelihood functions according to the multipath echo parameters to obtain current likelihood function values, and updating the historical optimal positions of the particles and the historical optimal positions of the particle swarm according to the current likelihood function values;
Step S130, performing convergence calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets the preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
step S140, if the calculation result does not meet the preset convergence condition, the cognition factor of the particle swarm optimization algorithm is adaptively updated according to the convergence calculation result, meanwhile, the speed and the position of each particle are updated, and the next iterative calculation is performed until the convergence calculation result meets the preset convergence condition;
step S150, maximum likelihood estimation of the target position is carried out according to the maximum likelihood estimation value of the multi-observation likelihood function.
In this embodiment, for the problem of radar target position estimation in a complex multipath scene of a city, starting from the point of solving the maximum likelihood estimation of multiple observation target likelihood functions, the method based on online Particle Swarm Optimization (PSO) is used for solving the problem, the multiple observation likelihood functions are used as fitness functions of a particle swarm algorithm in the solving process, and the maximum likelihood estimation of the fast solving target position is realized through online ray tracing multipath prediction and an adaptive cognitive factor particle swarm optimization algorithm.
In this embodiment, the target multipath effect is abstracted into a target multi-observation problem, a multi-observation likelihood function of the target is constructed, a PSO algorithm is adjusted based on online ray tracking multipath prediction and adaptive cognitive factors, and the maximum likelihood estimation of the likelihood function is obtained through iterative optimization as the final target position estimation.
In step S100, a target located in a complex environment of a known city is detected by using a radar, and multipath echo signals of the target are received. Wherein the environment in which the target is located is known, the known meaning here the geometrical information of the environment.
In step S110, all multipath echo parameters of the target are abstracted into a function related to radar position and target position, and are expressed as corresponding observation likelihood functions in combination with the omission condition of the paths, and multipath observation association hypothesis vectors are further constructed according to mutual independence of multipath measurement and clutter measurement in the radar measurement set (multipath echo signals), so as to obtain multiple observation likelihood functions of the target.
Specifically, modeling a multi-observation likelihood function of a target from multipath echo signals includes: first, assume that the position of the known radar isFor a position of- >All multipath echo parameters of the target can be abstracted into a function:
（1）
in the case of the formula (1),express goal->Is>Indicate->With two-pass echo pathThe radar measurement parameter vector, the specific form of which is determined by the form of the radar sensor, e.g. +.>The multipath echo time delay and angle of arrival received by the radar can be represented. The radar multipath echo signals can be equivalently observed by the corresponding mirror image virtual radar on the target, and can be obtained through calculation by a ray tracking method.
Combining echo missed detection situations possibly occurring in actual radar measurement, the firstBernoulli for strip multipath measurementThe corresponding likelihood function is denoted +.>. Combining the measurement set from clutter>The set of radar total measurements can be expressed as:
（2）
in the formula (2) of the present invention,representing the total measurement of the radar, i.e. the multipath echo signal, the measurement set +.>The measurement of the clutter from the clutter and the measurement of the multipath components are independent, the measurement set +.>Likelihood function of +.>Is the product of the components.
Further, a multipath observation correlation is constructed, assuming a vectorConstructing a multi-observation likelihood function of the target according to the acquired multipath echo signals, wherein the corresponding observation set of the target is +. >Is a subset division of the associated hypothesis set +.>The likelihood probability summation under the assumption of all effective correlations in the model is that the multi-observation likelihood function of the target can be obtained as follows:
（3）
and then, solving the maximum likelihood estimation value of the multi-observation likelihood function of the target by adopting an optimized PSO algorithm, wherein the maximum likelihood estimation value is the maximum likelihood estimation of the target position. And the solving process thereof is the contents in step S120 to step S140.
Particle Swarm Optimization (PSO) algorithm is a typical gradient-free search algorithm based on intelligent swarm optimization, and originates from the predation process of the swarm, and the algorithm core is to restrict the motion direction of the whole swarm in a target solving space through the most information sharing of the swarm, so that the optimal solution of the problem is obtained.
In this embodiment, the random particles in the target state space are used to sample the multiple observation likelihood functions, and the historical optimal positions of the particles and the historical optimal positions of the particle swarm are recorded.
Specifically, in step S120, geometric prior information of a scene where the target is located is obtained, and when an online particle swarm optimization algorithm is adopted to perform iterative solution, each particle position in the particle swarm is initialized according to the geometric prior information of the scene.
When the iterative solving process of the online particle swarm optimization algorithm is described, a simulation experiment performed according to the method can be combined. As shown in fig. 2, a multipath scene of a simulation experiment is shown, in which a rectangular block represents a building in the scene, and the coordinates of key points of the building are marked in the image, and the coordinates of the key points are known prior information of scene geometry. Position coordinates of radar RThe main lobe width is ∈>The center direction is the anticlockwise rotation of the x-axis direction +.>Position coordinates of target T->. First, a particle swarm is initialized, the particle number is set to 50, the maximum iteration number is set to 200, the current position of each particle is represented by a dot "," arrow indicates the speed of each particle, asterisks "/-j->"means the individual historical optimal position of each particle.
As shown in fig. 3, in the random initial state of the PSO algorithm, there is a local optimum in the random initial state of the visible image, and most particles move to the local optimum. For each particle in the particle swarm, the corresponding multipath echo parameter is calculated in real time through online ray tracing, then the multi-observation likelihood function is calculated according to the multipath echo parameter obtained by calculation of each particle, and the multi-observation likelihood function is used as the fitness function of the PSO algorithm in the method.
In this embodiment, in each iterative calculation, firstly, on-line ray tracking is utilized to calculate multipath echo parameters in real time according to the positions of each particle obtained in the previous iteration, that is, the current positions of each particle, and multiple observation likelihood functions are calculated according to each multipath echo parameter to obtain current likelihood function values, and meanwhile, the historical optimal positions of each particle and the historical optimal positions of particle swarm are updated according to the current likelihood function values.
Next, in step S130, a convergence calculation is performed according to the updated particle swarm history optimal position and the current particle positions, and the following formula is adopted:
（4）
wherein ,；
in the formula (4) of the present invention,represents particle swarm assembly, < >>Represents the historic optimal position of the center particle, i.e. the particle swarm,representation->At->Is->Particles contained in the neighborhood->Representing the potential of the population of particles.
In the present embodiment, the convergence is defined based on the number of particles contained in the neighborhood of the center particle, that is, the convergence(abbreviated as->) The concentration degree of the particle swarm near the central particle is represented, the change trend of the particle swarm reflects the convergence degree of the PSO algorithm, and the higher the particle concentration degree is, the closer the algorithm is to convergence.
Based on the definition of the convergence, when local optimum exists, the basic PSO algorithm can oscillate near the center particle, and the convergence speed of the algorithm is reduced. Therefore, in order to solve the concussion problem, a self-adaptive cognitive factor adjustment PSO algorithm based on convergence degree is provided, and individual cognitive factors and social cognitive factors are adjusted in real time according to the convergence degree of the particle swarm to control the overall movement direction of the particle swarm.
In step S130, the calculated convergence is compared with a preset threshold, if the calculated convergence is smaller than the threshold, the algorithm is converged, the iterative calculation can be stopped, and the likelihood function value obtained at present is the maximum likelihood estimation value.
If the convergence is greater than the threshold, continuing to perform the next iterative computation, and then adaptively updating the cognitive factors of the particle swarm optimization algorithm according to the convergence computation result before, and simultaneously updating the speed and the position of each particle.
The ideal PSO algorithm has better wide area searching performance in the initial stage, and the particle swarm can quickly converge towards the central particle along with the increase of the iteration times, so that the self-adaptive individual cognitive factorThe adaptive social cognition factor should be 0 along with the convergence degree along with the trend of the iteration number>Increasing and increasing.
In this embodiment, the following formula is used to update the cognitive factor used in calculating the particle velocity according to the convergence calculation result:
（5）
（6）
in the formula (5) and the formula (6),representing convergence calculation result,/->Representing individual cognitive factors->Representing social group cognitive factors.
Next, the moving speed and the position of each particle are updated according to the updated cognitive factors, including: calculating according to the positions of the particles obtained in the previous iteration, namely the positions of the current particles and the moving speed of the particles in the previous iteration, obtaining the moving speed of the corresponding particles in the current iteration, and calculating according to the moving speed of the particles in the current iteration, so as to obtain the positions of the particles after moving, namely the positions of the particles in the next iteration.
Specifically, the calculation is performed according to the position of each particle obtained in the previous iteration, that is, the position of each current particle and the moving speed of each particle in the previous iteration, so as to obtain the moving speed of the corresponding particle in the current iteration, and the following formula is adopted:
（7）
in the formula (7) of the present invention,represents the number of iterations, +.>Indicate->The individual particles are at->Wheel speed->Representing inertial factors, ++>Indicate->The individual particles are at->Wheel position-> and />Is cognitive factor, and represents independent individual cognitive factor and social group cognitive factor respectively,/for> and />Respectively represent to the firstIteration up to->Historical optimal position of individual particles and all particles, < >> and />The individual awareness weight and the social awareness weight are represented, respectively.
Specifically, the calculation is performed according to the moving speed of each particle in the current iteration, so as to correspondingly obtain the moved position of each particle, namely, the position of each particle in the next iteration adopts the following formula:
（8）
specifically, in each iterative calculation, updating the individual optimal position and the population optimal position of each particle and calculating the convergence of the current particle swarmIf the algorithm reaches the convergence threshold condition +.>Or the maximum iteration number is reached, the algorithm is terminated, and the current population optimal value position is output as the maximum likelihood estimation result. Otherwise, adaptively updating the cognitive factor of PSO algorithm +. > and />And updating the speed and the position of each particle at the same time, and carrying out the next iteration cycle until the convergence condition is met to obtain the maximum likelihood estimation of the target position.
As shown in fig. 4, the particle state at the 24 th iteration is that the center particle is a local optimum, a small part of particles are near the center particle, a large part of particles are still far away, and the convergence of the particle group is low. Fig. 5 shows the state of the particles at the 45 th iteration, after a period of iteration, the particle swarm finds a new center particle, and most of the particles converge toward the new center particle, but the individual historic optimal positions of most of the particles do not move, although the particles move toward the center particle. Fig. 6 shows the state at iteration 80, where all particles converge near the center particle and the algorithm terminates.
Fig. 7 and fig. 8 respectively show graphs of the convergence of the basic PSO algorithm and the adaptive cognitive factor adjustment PSO algorithm proposed by the present invention according to the number of iterations. In FIG. 7, the basic PSO algorithm is fixed when the particle converges near the center particle, if its individual optimum position is far away at this timeThe particle velocity is high, so that each time the particle oscillates near the center particle, the convergence curve shows up as fluctuation of the convergence of the current particle, and the optimal particle cannot converge. In FIG. 8, the adaptive cognitive factors proposed in the method avoid the above problems, and a larger individual cognitive factor is set in the search phase +. >The random search function is exerted, and the gradual convergence change in the corresponding graph is realized; decrease +.>The convergence speed of the particle swarm to the center is accelerated, and the particle swarm can be converged to the center particle rapidly.
Fig. 9 and 10 are graphs showing the variation of the estimation performance with the number of iterations, where fig. 9 is the maximum likelihood function value among all particles in each iteration, and fig. 10 is the error between the particle position corresponding to the maximum likelihood value and the true value. It can be seen that the convergence rate of the basic PSO is faster than the method proposed herein (referred to as the present method), however, it is difficult to satisfy the convergence termination condition because it faces the convergence oscillation problem. When the iteration number is 24, the method falls into the local optimum shown in fig. 4, the likelihood function and the position estimation in fig. 9 and 10 reside in the local optimum, and according to the convergence condition of the basic PSO algorithm, after the objective function value is unchanged by a certain iteration number, the algorithm is stopped and the result is output. However, as can be seen from fig. 8, the algorithm converges very slowly at this time, so that the convergence condition is not satisfied, and after iteration is continued, the algorithm breaks away from the local optimum and finally converges correctly to the target position.
The simulation experiment result can also show that compared with the traditional PSO algorithm which is judged through the difference value of the objective function and the iteration times, the convergence criterion of the method is more robust, the local optimum and the global optimum can be correctly identified, and the algorithm is prevented from falling into the local optimum.
In one embodiment, the flow of the method is shown in FIG. 11.
In the multi-observation target position estimation method based on the online particle swarm optimization, the multipath propagation process of the radar signal can be approximated by geometrical optics by using the far-field condition, so that the propagation path calculation of the radar to any target position in the known urban environment can be realized by using the ray tracing technology. The multipath effect of the target in the urban environment is modeled into a multi-observation problem of the radar on the target, and the target state estimation problem is converted into a maximum likelihood estimation problem by constructing a multi-observation likelihood function of the target. The method provides a self-adaptive cognitive factor adjustment Particle Swarm Optimization (PSO) algorithm, firstly, a particle swarm samples a target state space, carries out online ray tracing multipath prediction on each particle, calculates a multi-observation likelihood function value of each particle, then adaptively updates and adjusts the cognitive factor of the PSO algorithm based on the proposed convergence degree, continuously updates the speed and the position of each particle, and finally, continuously carries out iterative loop until the convergence to global optimum, namely, solves the maximum likelihood estimation of the multi-observation function, and obtains the optimal position estimation of the target. The existing method is mostly based on specific multipath scenes, and the method can adapt to complex heterogeneous multipath scenes, and can still realize effective estimation of the target position under the conditions of unstable or changed radar position and scene parameters. Compared with the traditional PSO algorithm which is based on the difference value of the objective function and the judgment convergence of the iteration times, the convergence criterion provided by the method is more robust, can correctly identify local although local and global optima, and avoids the algorithm from falling into the local optima. Based on the convergence criterion, the method provides a self-adaptive cognitive factor for adjusting the PSO algorithm, so that the algorithm plays a role of random search by a larger cognitive factor when the convergence is lower, and reduces the cognitive factor to accelerate the convergence speed of the particle swarm to the center when the convergence is higher, thereby avoiding the problem that the basic PSO algorithm can oscillate when converging to the vicinity of the center particle.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 12, there is provided a multi-observation target position estimating apparatus based on online particle swarm optimization, comprising: a multipath echo signal acquisition module 200, a multi-observation likelihood function construction module 210, a first iterative computation module 220, a second iterative computation module 230, a third iterative computation module 240, and a target position estimation module 250, wherein:
A multipath echo signal acquisition module 200, configured to acquire multipath echo signals of a target;
a multi-observation likelihood function construction module 210, configured to construct a multi-observation likelihood function of the target according to the multipath echo signal, and perform iterative solution on a maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
a first iterative computation module 220, configured to calculate, in each iterative computation, multipath echo parameters in real time by using online ray tracking according to each particle position obtained in the previous iteration, and calculate the multiple observation likelihood functions according to each multipath echo parameter to obtain a current likelihood function value, and update each particle historical optimal position and each particle swarm historical optimal position according to the current likelihood function value;
the second iterative computation module 230 is configured to perform convergence computation according to the updated historical optimal position of the particle swarm and the current positions of the particles, and if the computation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
the third iterative computation module 240 is configured to adaptively update the cognitive factor of the particle swarm optimization algorithm according to the convergence computation result if the computation result does not meet the preset convergence condition, update the speed and the position of each particle at the same time, and perform the next iterative computation until the convergence computation result meets the preset convergence condition;
The target position estimation module 250 is configured to obtain a maximum likelihood estimation of the target position according to the multiple observation likelihood function maximum likelihood estimation value.
For specific limitations on the multi-observation target position estimation apparatus based on online particle swarm optimization, reference may be made to the above limitation on the multi-observation target position estimation method based on online particle swarm optimization, and the description thereof will be omitted. The modules in the multi-observation target position estimation apparatus based on online particle swarm optimization can be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 13. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a multi-observation target position estimation method based on online particle swarm optimization. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 13 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring a multipath echo signal of a target;
constructing a multi-observation likelihood function of the target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
in each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the previous iteration, namely the current particle position, calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
Performing convergence degree calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
if the calculation result does not meet the preset convergence condition, adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result, simultaneously updating the speed and the position of each particle, and performing the next iterative calculation until the convergence calculation result meets the preset convergence condition;
and obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a multipath echo signal of a target;
constructing a multi-observation likelihood function of the target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
in each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the previous iteration, namely the current particle position, calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
Performing convergence degree calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
if the calculation result does not meet the preset convergence condition, adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result, simultaneously updating the speed and the position of each particle, and performing the next iterative calculation until the convergence calculation result meets the preset convergence condition;
and obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (8)
1. The multi-observation target position estimation method based on online particle swarm optimization is characterized by comprising the following steps of:
acquiring a multipath echo signal of a target;
constructing a multi-observation likelihood function of the target according to the multipath echo signals, and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
In each iterative calculation, calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the previous iteration, namely the current particle position, calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
performing convergence degree calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets a preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
if the calculation result does not meet the preset convergence condition, adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result, simultaneously updating the speed and the position of each particle, and performing the next iterative calculation until the convergence calculation result meets the preset convergence condition;
and obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
2. The method for estimating positions of multiple observation targets according to claim 1, wherein geometric prior information of a scene in which the targets are located is obtained, and each particle position in a particle swarm is initialized according to the geometric prior information of the scene when an online particle swarm optimization algorithm is adopted for iterative solution.
3. The multi-observation target position estimating method according to claim 2, wherein the convergence is expressed as:
；
wherein ,；
in the above-mentioned description of the invention,representing the current position set of each particle,>represents the historic optimal position of the center particle, i.e. the particle swarm,representation->At->Is->Particles contained in the neighborhood->Representing the potential of the population of particles.
4. The method of estimating a position of a multi-observation target according to claim 3, wherein the step of adaptively updating the cognitive factor of the particle swarm optimization algorithm according to the convergence calculation result uses the following formula:
；
；
in the above-mentioned description of the invention,representing the convergence calculation result, +.>Representing individual cognitive factors->Representing social group cognitive factors.
5. The method of claim 4, wherein updating the speed and position of each particle after adaptively updating the cognitive factor of the particle swarm optimization algorithm based on the convergence calculation comprises:
calculating according to the positions of the particles obtained in the previous iteration, namely the positions of the current particles and the moving speed of the particles in the previous iteration, and obtaining the moving speed of the corresponding particles in the current iteration;
and calculating according to the moving speed of each particle in the current iteration, and correspondingly obtaining the moved position of each particle, namely the position of each particle in the next iteration.
6. The method for estimating a position of a multiple observation target according to claim 5, wherein the calculation is performed according to the position of each particle obtained in the previous iteration, that is, the position of each current particle, and the moving speed of each particle in the previous iteration, and the moving speed of the corresponding particle in the current iteration is obtained by using the following formula:
；
in the above-mentioned description of the invention,represents the number of iterations, +.>Indicate->The individual particles are at->Wheel speed->Representing inertial factors, ++>Indicate->The individual particles are at->Wheel position-> and />Respectively representing the individual cognitive factors and the social group cognitive factors,/for each individual> and />Respectively represent to the firstIteration up to->Historical optimal position of individual particles and particle groups, < >> and />The individual awareness weight and the social awareness weight are represented, respectively.
7. The method for estimating a position of a multiple observation target according to claim 6, wherein the calculation is performed according to a moving speed of each particle at a current iteration, and the position of each particle after the movement is correspondingly obtained, that is, the position of each particle at a next iteration adopts the following formula:
。
8. a multi-observation target position estimation apparatus based on online particle swarm optimization, the apparatus comprising:
The multipath echo signal acquisition module is used for acquiring multipath echo signals of the target;
the multi-observation likelihood function construction module is used for constructing a multi-observation likelihood function of the target according to the multipath echo signals and carrying out iterative solution on the maximum likelihood estimation value of the multi-observation likelihood function by adopting an online particle swarm optimization algorithm;
the first iterative computation module is used for calculating multipath echo parameters in real time by utilizing online ray tracking according to the position of each particle obtained in the last iteration, namely the current particle position, and calculating the multi-observation likelihood function according to each multipath echo parameter to obtain a current likelihood function value, and updating the historical optimal position of each particle and the historical optimal position of a particle swarm according to the current likelihood function value;
the second iterative calculation module is used for carrying out convergence calculation according to the updated particle swarm historical optimal position and the current particle positions, and if the calculation result meets the preset convergence condition, the current likelihood function value is the maximum likelihood estimation value;
the third iterative computation module is used for adaptively updating the cognitive factors of the particle swarm optimization algorithm according to the convergence degree computation result if the computation result does not meet the preset convergence condition, updating the speed and the position of each particle at the same time, and performing the next iterative computation until the convergence degree computation result meets the preset convergence condition;
And the target position estimation module is used for obtaining the maximum likelihood estimation of the target position according to the maximum likelihood estimation value of the multi-observation likelihood function.
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