CN104678382A - Long-distance high-precision distance measuring method applicable to spread spectrum system communication measurement and control system - Google Patents

Abstract

The invention discloses a long-distance high-precision distance measuring method applicable to a spread spectrum system communication measurement and control system. According to the technical scheme adopted by the invention, the method comprises the following steps: 1) acquiring data; 2) analyzing the data; 3) establishing a model; 4) performing Kallman filtering; 5) outputting the data. According to the method, an optimized Kallman filtering method is adopted and is innovatively applied to a long-distance distance measuring system, and the forecast estimation characteristic of Kallman filtering is fully utilized, so that the precision of the distance measuring system is increased by one order of magnitude, therefore the problems of complicated system and low precision of a long-distance distance measuring method are solved, and a favorable effect is achieved. The method has the characteristics of no increase in system complexity, easiness for system integration and easiness for software implementation, and is mainly applied to the field of wireless data chains.

Description

A kind of be applicable to spread spectrum system communication TT&C system under remote high-precision distance-finding method

Technical field

The invention belongs to wireless data chain field, relate to a kind of high precision wireless electricity navigation locating method of related data chain, especially a kind of remote high-precision distance-finding method of spread spectrum system communication TT&C system.

Background technology

In radio communication, because signal transmission environment is complicated and changeable, multiple navigation locating method is needed to ensure system reliability service.Radio navigation location malfunctioning as satellite positioning signals such as GPS time one navigation mode for subsequent use, range of application widely, and precision distance measurement be ensure radio navigation location necessary means.In current TT&C system, distance-finding method mainly contains side-tone ranging and pseudo-random code ranging.

Side-tone ranging utilizes sinusoidal signal to carry out range observation through coming and going apart from the change of corresponding phase place.Wherein low side tone ensures range finding distance, and high side tone ensures distance accuracy.But when distance increases, pure tone ranging system, due to features such as equipment complexity, poor anti jamming capability, is difficult to realize telemeasurement.

As shown in Figure 1, although pseudo-random code ranging has in spread spectrum system communication TT&C system realize the features such as convenient, flexible, antijamming capability is strong, but the measuring distance due to common pseudo-random code ranging depends on pseudo-code length, distance accuracy is limited to clock rate, therefore requiring that medium-long range range finding just requires that pseudo-code is long as far as possible, this and the TT&C system that communicates require that fast Acquisition forms contradiction with tracking; Distance accuracy is also limited to the clock frequency of system simultaneously.

Therefore, how to ensure fast Acquisition and the tracking performance of telecommunication TT&C system, neither increase substantially system complexity (system clock frequency raising can improve system complexity), the high precision of long-range range observation can be ensured again, become problem demanding prompt solution.

Summary of the invention

The invention discloses a kind of be applicable to spread spectrum system communication TT&C system under remote high-precision distance-finding method, aim to provide a kind of precision distance measurement method be applicable under telecommunication TT&C system, by carrying out filtering operation to a upper moment measured value and current time estimated value, finally reach the object of precision distance measurement.

The present invention adopts following technical scheme:

This method divides following a few step to carry out:

1) image data:

Receive terminal and quantize receiving spread frequency signal by AD sampling thief, carry out despreading by correlator, extract signal as range finding and the input of resolving module that communicates;

As shown in Figure 3, receive terminal and quantize receiving spread frequency signal by AD sampling thief, carry out despreading by correlator, the useful signal disappeared under noise is extracted, as range finding and the input of resolving module that communicates.In correlator design process, pseudo-random code is longer, and capture time is longer, and in the system that spreading rate is limited, it is insufferable that common pseudo-random code ranging realizes the find range time overhead that brings and system complexity of remote high-precision.Therefore, common Pseudo Code Ranging Method measuring distance is limited to pseudo-code length, and distance accuracy is limited to spreading rate and system clock.

2) data are analyzed:

Dynamic model is set up to the motion of measured object, and optimal estimation method is applied to dynamic model, filtering is carried out to range error and estimates process;

Analyze above-mentioned calculating process, we find that the synchronous error of system and range error are the critical quantity of the method, and namely the process of remote high-precision range finding is reduce the processes of these two amounts; Thus, namely the method for synchronous+Kalman filtering that we creatively can apply spread spectrum, also set up dynamic model to the motion of measured object, and optimal estimation method is applied to dynamic model, filtering is carried out to range error and estimates process, just can effectively reduce error, improve distance accuracy;

3) Modling model:

Be reference point with gauger, the distance between measurand and gauger, as measurand coordinate figure in a coordinate system, sets up system model;

The measured value of Pseudo Code Ranging Method is the distance measurement value based on measuring in real time, the distance situation of change namely between a certain moment measurand and gauger; So equivalence can set up one-dimensional coordinate system, take namely gauger as reference point, the distance between measurand and gauger is measurand coordinate figure in the coordinate system, and state equation is:

$\left[\begin{array}{c}\stackrel{\·}{x}\left(t\right)\\ \stackrel{\·\·}{x}\left(t\right)\\ \stackrel{\·\·\·}{x}\left(t\right)\end{array}\right]=\left[\begin{array}{ccc}0& 1& 0\\ 0& 0& 1\\ 0& 0& -1/\mathrm{\τ}\end{array}\right]\left[\begin{array}{c}x\left(t\right)\\ \stackrel{\·}{x}\left(t\right)\\ \stackrel{\·\·}{x}\left(t\right)\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ 1/\mathrm{\τ}\end{array}\right]\stackrel{\‾}{\mathrm{\α}}\left(t\right)+\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]\mathrm{\ω}\left(t\right)$

Wherein x (t) is distance value, for a variability of distance, for the secondary variability of distance, for three variabilities of distance, for the average of current acceleration, τ is time constant, and ω (t) is white Gaussian noise;

4) Kalman filtering:

Initialization system parameter and initial value, carry out Kalman filtering; The renewal equation of Kalman filtering:

$\left\{\begin{array}{c}{X}_i={\mathrm{\Φ}}_{i,i-1}{X}_{i-1}+B_{i-1}{u}_{i-1}+{\mathrm{\Γ}}_{i-1}{W}_{i-1}\\ Z_i=H_i{X}_i+Y_i+V_i\end{array}\right.$

Wherein, X _ifor the i moment by estimated state, Φ _{i, i-1}for the ti-1 moment is to the Matrix of shifting of a step in ti moment; for system noise drives battle array; H _ifor measuring battle array; V _ifor measurement noise sequence; W _i-1for system incentive noise sequence, { ui} and { the determinacy list entries that Yi} is known;

5) data are exported:

By above-mentioned steps, filtering is carried out in real time to range error and estimates and correct, progressively reduce range error, export ranging data; Reach the object of remote high-precision range finding.

The present invention relies on telecommunication TT&C system and develops, and this system adopts Direct-Spread system, and require to catch to be not more than 10ms with lock in time, range error is not more than 0.1m.The invention solves said system requirement.

Good effect of the present invention:

Owing to present invention employs the kalman filter method of optimization, be applied to long-distance ranging system innovatively, take full advantage of the predicted estimate characteristic of Kalman filtering, thus make the precision of range measurement system improve an order of magnitude, thus also solve the problem that the complicated precision of long-distance ranging method system is not high, achieve good effect.

This method is based on following thought: common Pseudo Code Ranging Method has the advantage being easy to realize, resource utilization is high under spread spectrum system, but measuring distance is limited to pseudo-code length, and distance accuracy is limited to spreading rate and system clock; And kalman filtering theory is ripe, by carrying out filtering operation to a upper moment measured value and current time estimated value, systematic error can be reduced; In conjunction with both advantages above-mentioned, propose the precision distance measurement method based on spread spectrum system telecommunication TT&C system, and successfully the method is applied in relevant Data-Link system.

This method, compared with current additive method, has and does not increase system complexity, is easy to the system integration, is easy to the feature of software simulating.

Accompanying drawing explanation

Fig. 1 is common Pseudo Code Ranging Method schematic diagram.

Fig. 2 is the Pseudo Code Ranging Method schematic diagram after this method is improved.

Fig. 3 is the system model schematic diagram of whole method.

The design sketch that Fig. 4 emulates for this method measuring error.

Embodiment

Below, by reference to the accompanying drawings and specific embodiment, invention is further described.

Embodiment 1

As shown in Figure 3, receive terminal and quantize receiving spread frequency signal by AD sampling thief, carry out despreading by correlator, the useful signal disappeared under noise is extracted, as range finding and the input of resolving module that communicates.In correlator design process, pseudo-random code is longer, and capture time is longer, and in the system that spreading rate is limited, it is insufferable that common pseudo-random code ranging realizes the find range time overhead that brings and system complexity of remote high-precision.Therefore, common Pseudo Code Ranging Method measuring distance is limited to pseudo-code length, and distance accuracy is limited to spreading rate and system clock.

Common Pseudo Code Ranging Method measuring distance is limited to pseudo-code length, and distance accuracy is limited to spreading rate and system clock.In order to the remote high-precision of pseudo-random code ranging under communication TT&C system can be ensured, must common Pseudo Code Ranging Method be improved, as shown in Figure 2.On this basis, we set up dynamic model to the motion of measured object, and optimal estimation method is applied to dynamic model, carry out filtering and estimate process, just can effectively reduce error, improve distance accuracy to range error.

Therefore, key is the foundation of dynamic model and choosing of optimal estimation method.As from the foregoing, the measured value of Pseudo Code Ranging Method is the distance measurement value based on measuring in real time, the distance situation of change namely between measured object of a certain moment and gauger.So equivalence can set up one-dimensional coordinate system, be namely reference point with gauger, then the distance between measured object and gauger is measurand coordinate figure in the coordinate system.Set up preliminary dynamic model on this basis.State equation is:

$\left[\begin{array}{c}\stackrel{\·}{x}\left(t\right)\\ \stackrel{\·\·}{x}\left(t\right)\\ \stackrel{\·\·\·}{x}\left(t\right)\end{array}\right]=\left[\begin{array}{ccc}0& 1& 0\\ 0& 0& 1\\ 0& 0& -1/\mathrm{\τ}\end{array}\right]\left[\begin{array}{c}x\left(t\right)\\ \stackrel{\·}{x}\left(t\right)\\ \stackrel{\·\·}{x}\left(t\right)\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ 1/\mathrm{\τ}\end{array}\right]\stackrel{\‾}{\mathrm{\α}}\left(t\right)+\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]\mathrm{\ω}\left(t\right)$

Wherein x (t) is distance value, for a variability of distance, for the secondary variability of distance, for three variabilities of distance, for the average of current acceleration, τ is time constant, and ω (t) is white Gaussian noise.The framed structure of whole system is as shown in Figure 3:

Secondly, consider the complexity of calculating, convergence time, and the unbiasedness of convergence result, choose kalman filter method in the present invention, the renewal equation of the method

$\left\{\begin{array}{c}{X}_i={\mathrm{\Φ}}_{i,i-1}{X}_{i-1}+B_{i-1}{u}_{i-1}+{\mathrm{\Γ}}_{i-1}{W}_{i-1}\\ Z_i=H_i{X}_i+Y_i+V_i\end{array}\right.$

Wherein, X _ifor the i moment by estimated state, Φ _{i, i-1}for t _i-1moment is to t _ithe Matrix of shifting of a step in moment; for system noise drives battle array; H _ifor measuring battle array; V _ifor measurement noise sequence; W _i-1for system incentive noise sequence, { u _iand { Y _iknown determinacy list entries.

Under normal circumstances, estimated value is aimed at one by one with observed reading.In range finding application, range_rate change variation range and intensity are all smaller, and namely state mutation is very little.Experiment shows, the state change within the scope of this can not affect Kalman filtering effect, also can not introduce time delay.So the distance value before and after filtering is aimed at one by one.

According to the relative distance relation between measured object and gauger, a segment distance curve can be simulated, as actual value.Add white Gaussian noise on this basis, simulation raw observation, as the input of Kalman filtering, the range error between comparing before and after filtering.For ease of emulation, parameter choose range_rate change variance is 1, observation noise variance R=1.2 ², acceleration time correlation constant is 1, and time correlation constant is 0.5, and simulation result as shown in Figure 4.

As can be seen here, can prove that the long-distance ranging precision of this common invention is not more than 0.1m by emulation, meet system requirements completely.

The foregoing is only better possible embodiments of the present invention, not thereby limit to the scope of the claims of the present invention, therefore the equivalent structure change that every utilization instructions of the present invention and accompanying drawing content are done, be all contained in protection scope of the present invention.

Claims (3)

1. be applicable to the remote high-precision distance-finding method under spread spectrum system communication TT&C system, comprise:

1) image data: receive terminal and quantize receiving spread frequency signal by AD sampling thief, carry out despreading by correlator, extracts signal as range finding and the input of resolving module that communicates;

2) analyze data: dynamic model is set up to the motion of measured object, and optimal estimation method is applied to dynamic model, filtering is carried out to range error and estimates process;

3) Modling model: take gauger as reference point, the distance between measurand and gauger, as measurand coordinate figure in a coordinate system, sets up system model;

4) Kalman filtering: initialization system parameter and initial value, carries out Kalman filtering;

5) export data: by above-mentioned steps, filtering is carried out in real time to range error and estimates and correct, progressively reduce range error, export ranging data.

2. according to claim 1 be applicable to spread spectrum system communication TT&C system under remote high-precision distance-finding method, it is characterized in that, in step 3) Modling model, the state equation of described system model is:

$\left[\begin{array}{c}\stackrel{\·}{x}\left(t\right)\\ \stackrel{\·\·}{x}\left(t\right)\\ \stackrel{\·\·\·}{x}\left(t\right)\end{array}\right]=\left[\begin{array}{ccc}0& 1& 0\\ 0& 0& 1\\ 0& 0& -1/\mathrm{\τ}\end{array}\right]\left[\begin{array}{c}x\left(t\right)\\ \stackrel{\·}{x}\left(t\right)\\ \stackrel{\·\·}{x}\left(t\right)\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ 1/\mathrm{\τ}\end{array}\right]\stackrel{\‾}{\mathrm{\α}}\left(t\right)+\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]\mathrm{\ω}\left(t\right)$

Wherein x (t) is distance value, for a variability of distance, for the secondary variability of distance, for three variabilities of distance, for the average of current acceleration, τ is time constant, and ω (t) is white Gaussian noise.

3. according to claim 1 be applicable to spread spectrum system communication TT&C system under remote high-precision distance-finding method, it is characterized in that, the renewal equation in step 4) Kalman filtering:

$\left\{\begin{array}{c}{X}_i={\mathrm{\Φ}}_{i,i-1}{X}_{i-1}+B_{i-1}{u}_{i-1}+{\mathrm{\Γ}}_{i-1}{W}_{i-1}\\ Z_i=H_i{X}_i+Y_i+V_i\end{array}\right.$

Wherein, X _ifor the i moment by estimated state, for the ti-1 moment is to the Matrix of shifting of a step in ti moment; for system noise drives battle array; H _ifor measuring battle array; V _ifor measurement noise sequence; W _i-1for system incentive noise sequence, { ui} and { the determinacy list entries that Yi} is known.