CN103297087A - Arrival time estimation method for ultra-wideband positioning system - Google Patents

Abstract

Disclosed is an arrival time estimation method for an ultra-wideband positioning system. The arrival time estimation method mainly includes steps of firstly, sampling energy of a received signal; secondly, solving a decision threshold; thirdly, estimating the arrival time of a signal. The arrival time estimation method has the advantages that a threshold solving model is based on constant false alarm rate constraints, so that excellent arrival time estimation precision can be acquired in different ultra-wideband channel modes; an energy sequence is sorted preliminarily in a computation procedure, so that the computational complexity for completing arrival time estimation is reduced; an iteration threshold selection algorithm is independent, does not depend on acquisition of prior information of channels and can be applied to the actual ultra-wideband positioning system; the arrival time estimation method has high practical value and a wide application prospect in the technical field of signal detection and estimation.

Description

A kind of method of estimation time of advent of ultra broadband navigation system

Technical field

The present invention relates to a kind of method of estimation time of advent of impulse ultra-wideband signal, relate in particular to a kind of method of estimation time of advent of ultra broadband navigation system, belong to input and estimation technique field, it is that the head that utilizes interative computation to detect multipath signal reaches the path, thereby estimates the time of advent of signal.The present invention is applicable to based on estimating the time of advent of the navigation system ranging process of impulse ultra-wideband signal.

Background technology

In recent years, along with the high speed development of mobile communication and technology of Internet of things, people increase day by day to the demand of hi-Fix business.The pulse ultra-broad band technology can obtain very high temporal resolution and good anti-multipath performance, and this shows one's talent it from numerous wireless location technologies, becomes to realize pinpoint preferred option, and is extensive in the application prospect in fields such as military affairs, medical treatment, industry.

In a wireless location system, be to obtain by the signal of propagating between evaluating objects node and some reference nodes to the estimation of the positional information of destination node, common whole estimation procedure comprises parameter extraction and two steps of location compute.At first, system extracts the parameter information-related with destination node location from the signal of propagating, adopt corresponding location algorithm to calculate the positional information of destination node then from relevant parameter.According to the difference of the parameter that obtains, location technology can be divided into based on the arrival angle, based on the reception signal strength signal intensity with based on these three kinds of estimation modes time of advent.The invention belongs to last a kind of mode, by estimating to arrive the distance that time delay is calculated transmitting-receiving two-end, taken full advantage of the higher temporal resolution of ultra-broadband signal, can embody the advantage of ultra broadband hi-Fix.

Summary of the invention

1, purpose: the purpose of this invention is to provide a kind of method of estimation time of advent of ultra broadband navigation system, this method provides a kind of head at impulse ultra-wideband signal to reach path detection method.Relate to an iterative process that is used for calculating the optimum decision thresholding in the described method, it adopts horizontal false alarm rate constraint as resolving model, use the greatest hope value-based algorithm to finish iteration, can under prior information condition of unknown under the concrete channelling mode, estimate the time of advent of ultra-broadband signal thus.

2, technical scheme: Fig. 1 is the system flow chart that the present invention relates to.Wherein mainly contain two parts: estimate the energy sampled of signal and the time of advent of signal.In the pretreatment stage of signal, finish to the received signal under the synchronous situation of frame one-level, earlier signal is carried out the square law detection, again the energy signal of output is sampled, obtain receiving the energy sampled sequence of signal.In the estimation stages time of advent, utilize iteration thresholding selection algorithm from the energy sampled sequence, to calculate decision threshold, the head that uses this thresholding to detect signal from the energy sampled sequence reaches the path, finishes the time of advent of signal and estimates.

In sum, the method for estimation time of advent of a kind of ultra broadband navigation system of the present invention, these method concrete steps are as follows:

Step 1: the energy sampled that receives signal

Ultra-broadband signal after the frame synchronization is carried out the square law detection, obtain receiving the energy signal of signal; Again this energy signal is sampled, obtain receiving the energy sampled sequence of signal.

The pulse ultra-broadband signal can be expressed as during the jumping that receives

$r\left(t\right)=\underset{j=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{d}_j{w}_{\mathrm{mp}}(t-{\mathrm{jT}}_f-c_j{T}_c-{\mathrm{\τ}}_{\mathrm{toa}})+n\left(t\right)$

Wherein, T _fAnd T _cBe respectively frame length and chip lengths; d _jBe the polarity of j frame signal, τ _ToaBe the time of advent of signal; N (t) is white Gaussian noise, and average is zero, and variance is δ ², bilateral power spectral density is N ₀/ 2; c _jBe the jumping time-code that distributes in order to prevent the signal conflict between the different user, it has determined the position of pulse chip in a frame, and the time-hopping sequence that k user is assigned to satisfies

w _Mp(t) the multipath impulse waveform for receiving can be expressed as

$w_{\mathrm{mp}}\left(t\right)=\sqrt{E}\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{a}_{l}w(t-{\mathrm{\τ}}_l)$

W (t) is the individual pulse waveform of energy normalized, and the duration is T _pL is multipath quantity; a _lAnd τ _lBe respectively attenuation coefficient and the retardation coefficient of channel; E is pulse energy.The signal that receives is by after the energy integral, with sampling interval T _bEnergy signal is sampled.Make N _fBe the number of the frame in each symbol, the energy sequence of each symbol is

$z\left[n\right]=\underset{j=1}{\overset{N_f}{\mathrm{\Σ}}}{\∫}_{(j-1)T_f+(c_j+n-1)T_b}^{(j-1)T_f+(c_j+n)T_b}{\left|r\left(t\right)\right|}^2\mathrm{dt}$

Step 2: the resolving of decision threshold

Energy sampled sequence to signal sorts, and calculates an energy threshold value by iteration optimization under the constraint of certain constant false alarm rate, is used for to received signal head and reaches the path and adjudicate.

After signal was finished frame synchronization, evenly distributed the time of advent in first footpath in a frame, considers crosstalking of interframe, observation is set to 1.5 times frame length.Make T _ObBe to observe at interval, then sequence z[n] comprise

The sampled energy piece.Stack power piece comprising pure noise energy piece and signal and noise.

In K energy sampled piece of energy sequence, comprise two kinds of pure noise energy piece and noise signal stack power pieces.Wherein, the value of pure noise energy piece is obeyed card side, center and is distributed, and average is M δ ², variance is 2M δ ⁴, the degree of freedom is M=2BT _b+ 1, B is the bandwidth of signal; Noise signal stack power piece is then obeyed non-central card side and is distributed, and average is M δ ²+ E _n, variance is 2M δ ⁴+ 4 δ ²E _n, E _nSignal energy for this sampling block.The iteration threshold algorithm adopts Niemann-Pearson came hypothesis testing, and iteration goes out threshold value under the constant false alarm rate constraint, for the noise energy piece that card side, center distributes, false alarm rate P _FaAs follows with the relation of threshold value ξ:

$P_{\mathrm{fa}}=P\left(z\right[n]>\mathrm{\ξ})=\exp (-\frac{\mathrm{\ξ}}{{2\mathrm{\δ}}^2})\underset{k=0}{\overset{M/2-1}{\mathrm{\Σ}}}\frac{1}{k!}{\left(\frac{\mathrm{\ξ}}{{2\mathrm{\δ}}^2}\right)}^k$

Fig. 2 is the flow chart of the iteration thresholding selection algorithm among the present invention.This algorithm key be that the noise energy piece is being got rid of successively, in each iteration, finish the renewal of threshold value, thereby detect a part of noise energy piece, when all noise energy pieces all were detected, the performance of thresholding reached the estimated accuracy time of advent of algorithm optimum.

Step 3: estimate the time of advent of signal

The head that utilizes the decision threshold that obtains in the step 2 to detect signal from the energy sampled sequence reaches the path, and the head of signal reaches the estimated value that is time of arrival (toa) the time of advent in path.Under the condition of low signal-to-noise ratio, by estimating the time of advent that predetermined compensation policy is finished under the thresholding failure conditions.

After calculating the optimum gate limit value, can be expressed as the time of advent that head reaches place, path energy block

${\mathrm{\τ}}_{\mathrm{ITS}}=\{\underset{K}{\min }\left(k\right|z\left[k\right]{>\mathrm{\ξ}}_{\mathrm{opt}})-0.5\}\×T_b$

Under the condition of low signal-to-noise ratio, the difference that does not have conspicuousness between the sampled value of pure noise energy piece and noise signal stack power interblock, this may cause the threshold value of iteration can't finish the situation of detection, i.e. all energy sampled values in the threshold value great-than search sequence that goes out of iteration.Under the situation that thresholding lost efficacy, this method has adopted two kinds of compensation policies.First kind is with the signal time of advent of the time of advent of strong value in the observation sequence, and second kind is with the intermediate value of observation window signal time of advent.

Wherein, the basic procedure of the described iteration thresholding of step 2 selection algorithm is as follows

1) with observation sequence z[n] sort by ascending order, suppose that the top n energy block all is the noise piece;

2) utilize value and the false alarm probability P of this N energy block _FaCalculate corresponding threshold value ξ, the energy block less than this threshold value all is considered to the noise piece like this;

3) noise piece and the false alarm probability P that utilizes previous step to detect _FaRecomputate corresponding threshold value, the threshold value of renewal can detect one group of new noise piece again;

4) constantly repeat this process and reach preset value up to iterations, the threshold value that obtain this moment is designated as ξ _Opt

3, advantage and effect: the head that the present invention can detect in the multipath signal that receives reaches the path, and this time of advent, method of estimation mainly possessed following advantage:

1) thresholding involved in the present invention resolves model because be based on the constant false alarm rate constraint, can both obtain good time of advent of estimated accuracy in different ultra-wideband channel patterns.

2) computational process involved in the present invention has been carried out pre-sorting to energy sequence, has reduced and has finished the amount of calculation of estimating the time of advent.

3) iteration thresholding selection algorithm involved in the present invention has independence, does not rely on obtaining of channel prior information, can be applied among the actual ultra broadband navigation system.

Description of drawings

Fig. 1 is the system flow chart that the inventive method relates to

Fig. 2 is the iteration thresholding selection algorithm flow chart among the present invention

Embodiment

See Fig. 1, the method for estimation time of advent of a kind of ultra broadband navigation system of the present invention, these method concrete steps are as follows:

Step 1: the energy sampled that receives signal

The pulse ultra-broadband signal can be expressed as during the jumping that receives

$r\left(t\right)=\underset{j=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{d}_j{w}_{\mathrm{mp}}(t-{\mathrm{jT}}_f-c_j{T}_c-{\mathrm{\τ}}_{\mathrm{toa}})+n\left(t\right)$

Wherein, T _fAnd T _cBe respectively frame length and chip lengths; d _jBe the polarity of j frame signal, τ _ToaBe the time of advent of signal; N (t) is white Gaussian noise, and average is zero, and variance is δ ², bilateral power spectral density is N ₀/ 2; c _jBe the jumping time-code that distributes in order to prevent the signal conflict between the different user, it has determined the position of pulse chip in a frame, and the time-hopping sequence that k user is assigned to satisfies

w _Mp(t) the multipath impulse waveform for receiving can be expressed as

$w_{\mathrm{mp}}\left(t\right)=\sqrt{E}\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{a}_{l}w(t-{\mathrm{\τ}}_l)$

W (t) is the individual pulse waveform of energy normalized, and the duration is T _pL is multipath quantity; a _lAnd τ _lBe respectively attenuation coefficient and the retardation coefficient of channel; E is pulse energy.The signal that receives is by after the energy integral, with sampling interval T _bEnergy signal is sampled.Make N _fBe the number of the frame in each symbol, the energy sequence of each symbol is

$z\left[n\right]=\underset{j=1}{\overset{N_f}{\mathrm{\Σ}}}{\∫}_{(j-1)T_f+(c_j+n-1)T_b}^{(j-1)T_f+(c_j+n)T_b}{\left|r\left(t\right)\right|}^2\mathrm{dt}$

Step 2: the resolving of decision threshold

After signal was finished frame synchronization, evenly distributed the time of advent in first footpath in a frame, considers crosstalking of interframe, observation is set to 1.5 times frame length.Make T _ObBe to observe at interval, then sequence z[n] comprise

The sampled energy piece.Stack power piece comprising pure noise energy piece and signal and noise.

In K energy sampled piece of energy sequence, comprise two kinds of pure noise energy piece and noise signal stack power pieces.Wherein, the value of pure noise energy piece is obeyed card side, center and is distributed, and average is M δ ², variance is 2M δ ⁴, the degree of freedom is M=2BT _b+ 1, B is the bandwidth of signal; Noise signal stack power piece is then obeyed non-central card side and is distributed, and average is M δ ²+ E _n, variance is 2M δ ⁴+ 4 δ ²E _n, E _nSignal energy for this sampling block.The iteration threshold algorithm adopts Niemann-Pearson came hypothesis testing, and iteration goes out threshold value under the constant false alarm rate constraint, for the noise energy piece that card side, center distributes, false alarm rate P _FaGo into down with the relation of threshold value ξ

$P_{\mathrm{fa}}=P\left(z\right[n]>\mathrm{\ξ})=\exp (-\frac{\mathrm{\ξ}}{{2\mathrm{\δ}}^2})\underset{k=0}{\overset{M/2-1}{\mathrm{\Σ}}}\frac{1}{k!}{\left(\frac{\mathrm{\ξ}}{{2\mathrm{\δ}}^2}\right)}^k$

Fig. 2 is the flow chart of the iteration thresholding selection algorithm among the present invention.This algorithm key be that the noise energy piece is being got rid of successively, in each iteration, finish the renewal of threshold value, thereby detect a part of noise energy piece, when all noise energy pieces all were detected, the performance of thresholding reached the estimated accuracy time of advent of algorithm optimum.The basic procedure of this algorithm is as follows

1) with observation sequence z[n] sort by ascending order, suppose that the top n energy block all is the noise piece;

2) utilize value and the false alarm probability P of this N energy block _FaCalculate corresponding threshold value ξ, the energy block less than this threshold value all is considered to the noise piece like this;

3) noise piece and the false alarm probability P that utilizes previous step to detect _FaRecomputate corresponding threshold value, the threshold value of renewal can detect one group of new noise piece again;

4) constantly repeat this process and reach preset value up to iterations, the threshold value that obtain this moment is designated as ξ _Opt

Step 3: estimate the time of advent of signal

After calculating the optimum gate limit value, can be expressed as the time of advent that head reaches place, path energy block

${\mathrm{\τ}}_{\mathrm{ITS}}=\{\underset{K}{\min }\left(k\right|z\left[k\right]{>\mathrm{\ξ}}_{\mathrm{opt}})-0.5\}\×T_b$

Under the condition of low signal-to-noise ratio, the difference that does not have conspicuousness between the sampled value of pure noise energy piece and noise signal stack power interblock, this may cause the threshold value of iteration can't finish the situation of detection, i.e. all energy sampled values in the threshold value great-than search sequence that goes out of iteration.Under the situation that thresholding lost efficacy, this method has adopted two kinds of compensation policies.First kind is with the signal time of advent of the time of advent of strong value in the observation sequence, and second kind is with the intermediate value of observation window signal time of advent.

Claims (2)

1. the method for estimation time of advent of a ultra broadband navigation system, it is characterized in that: these method concrete steps are as follows:

Step 1: the energy sampled that receives signal

Ultra-broadband signal after the frame synchronization is carried out the square law detection, obtain receiving the energy signal of signal; Again this energy signal is sampled, obtain receiving the energy sampled sequence of signal;

The pulse ultra-broadband signal is expressed as during the jumping that receives

$r\left(t\right)=\underset{j=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{d}_j{w}_{\mathrm{mp}}(t-{\mathrm{jT}}_f-c_j{T}_c-{\mathrm{\τ}}_{\mathrm{toa}})+n\left(t\right)$

Wherein, T _fAnd T _cBe respectively frame length and chip lengths; d _jBe the polarity of j frame signal, τ _ToaBe the time of advent of signal; N (t) is white Gaussian noise, and average is zero, and variance is δ ², bilateral power spectral density is N ₀/ 2; c _jBe the jumping time-code that distributes in order to prevent the signal conflict between the different user, it has determined the position of pulse chip in a frame, and the time-hopping sequence that k user is assigned to satisfies

w _Mp(t) the multipath impulse waveform for receiving is expressed as

$w_{\mathrm{mp}}\left(t\right)=\sqrt{E}\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{a}_{l}w(t-{\mathrm{\τ}}_l)$

W (t) is the individual pulse waveform of energy normalized, and the duration is T _pL is multipath quantity; a _lAnd τ _lBe respectively attenuation coefficient and the retardation coefficient of channel; E is pulse energy, and the signal that receives is by after the energy integral, with sampling interval T _bEnergy signal is sampled, make N _fBe the number of the frame in each symbol, the energy sequence of each symbol is

$z\left[n\right]=\underset{j=1}{\overset{N_f}{\mathrm{\Σ}}}{\∫}_{(j-1)T_f+(c_j+n-1)T_b}^{(j-1)T_f+(c_j+n)T_b}{\left|r\left(t\right)\right|}^2\mathrm{dt}$

Step 2: the resolving of decision threshold

Energy sampled sequence to signal sorts, and calculates an energy threshold value by iteration optimization under constant false alarm rate constraint, is used for to received signal head and reaches the path and adjudicate;

After signal was finished frame synchronization, evenly distributed the time of advent in first footpath in a frame, considers crosstalking of interframe, observation is set to 1.5 times frame length; Make T _ObBe to observe at interval, then sequence z[n] comprise

The sampled energy piece; Stack power piece comprising pure noise energy piece and signal and noise;

In K energy sampled piece of energy sequence, comprise two kinds of pure noise energy piece and noise signal stack power pieces; Wherein, the value of pure noise energy piece is obeyed card side, center and is distributed, and average is M δ ², variance is 2M δ ⁴, the degree of freedom is M=2BT _b+ 1, B is the bandwidth of signal; Noise signal stack power piece is then obeyed non-central card side and is distributed, and average is M δ ²+ E _n, variance is 2M δ ⁴+ 4 δ ²E _n, E _nSignal energy for this sampling block; The iteration threshold algorithm adopts Niemann-Pearson came hypothesis testing, and iteration goes out threshold value under the constant false alarm rate constraint, for the noise energy piece that card side, center distributes, false alarm rate P _FaAs follows with the relation of threshold value ξ:

$P_{\mathrm{fa}}=P\left(z\right[n]>\mathrm{\ξ})=\exp (-\frac{\mathrm{\ξ}}{2{\mathrm{\δ}}^2})\underset{k=0}{\overset{M/2-1}{\mathrm{\Σ}}}\frac{1}{k!}{\left(\frac{\mathrm{\ξ}}{{2\mathrm{\δ}}^2}\right)}^k$

This iteration thresholding selection algorithm key be that the noise energy piece is being got rid of successively, in each iteration, finish the renewal of threshold value, thereby detect a part of noise energy piece, when all noise energy pieces all were detected, the performance of thresholding reached the estimated accuracy time of advent of algorithm optimum;

Step 3: estimate the time of advent of signal

The head that utilizes the decision threshold that obtains in the step 2 to detect signal from the energy sampled sequence reaches the path, the head of signal reaches the estimated value that is time of arrival (toa) the time of advent in path, under the condition of low signal-to-noise ratio, by estimating the time of advent that predetermined compensation policy is finished under the thresholding failure conditions;

After calculating the optimum gate limit value, be expressed as the time of advent that head reaches place, path energy block

${\mathrm{\τ}}_{\mathrm{ITS}}=\{\underset{K}{\min }\left(k\right|z\left[k\right]>{\mathrm{\ξ}}_{\mathrm{opt}})-0.5\}{\×T}_b$

Under the condition of low signal-to-noise ratio, the difference that does not have conspicuousness between the sampled value of pure noise energy piece and noise signal stack power interblock, this can cause the threshold value of iteration can't finish the situation of detection, i.e. all energy sampled values in the threshold value great-than search sequence that goes out of iteration; Under the situation that thresholding lost efficacy, adopt two kinds of compensation policies, first kind is the signal time of advent of the time of advent with the strongest value in the observation sequence, second kind is with the intermediate value of observation window signal time of advent.

2. the method for estimation time of advent of a kind of ultra broadband navigation system according to claim 1, it is characterized in that: the basic procedure of the described iteration thresholding of step 2 selection algorithm is as follows:

1) with observation sequence z[n] sort by ascending order, suppose that the top n energy block all is the noise piece;

2) utilize value and the false alarm probability P of this N energy block _FaCalculate corresponding threshold value ξ, the energy block less than this threshold value all is considered to the noise piece like this;

3) noise piece and the false alarm probability P that utilizes previous step to detect _FaRecomputate corresponding threshold value, the threshold value of renewal can detect one group of new noise piece again;

4) constantly repeat this process and reach preset value up to iterations, the threshold value that obtain this moment is designated as ξ _Opt

Images