DESCRIPTION

Method for Selecting Energy Threshold for Radio Signal

Technical Field

The present invention relates generally to estimating a time-of-arrival (TOA) of a radio signal, and more particularly to selecting an energy threshold for TOA estimation of an ultra- wideband (UWB) signal.

Background Art

Impulse radio ultra-wideband (IR-UWB) enables precise ranging and location estimation due to extremely fast and short duration pulses, e.g., billions of sub-nanosecond pulses per second. Accurate time-of-arrival (TOA) estimation of the received signal is a key aspect for precise ranging. However, received UWB signals can include hundreds of multipath components, which increase the difficulty of TOA estimation.

If a coarse timing estimate is available, then an energy of the received samples can be compared with an energy threshold. The first sample that exceeds the threshold can be used as an estimate of the TOA.

However, it is a problem to select an appropriate threshold. The threshold can be based on received signal statistics, i.e., the signal-to-noise ratio (SNR) or a channel realization. If the selected threshold is based solely on noise variance, then the variance of the noise needs to be determined,

Scholtz et al., "Problems in modeling UWB channels," Proc. IEEE Asilomar Conf. Signals, Syst. Computers, vol. 1, pp. 706-711, Nov. 2002.

One method uses a normalized threshold technique that assigns a threshold between minimum and maximum values of energy samples, see

U.S. Patent Application serial number 11/XXX ₃ XXX entitled "UWB

Ranging" and filed by Molisch et al. on July 18, 2005. However, there are two practical limitations to that method. It is difficult to estimate the SNR, and using only the SNR of the received signal does not account for individual channel realizations. This results in a suboptimal threshold selection.

Disclosure of Invention

The invention provides a method for estimation a time-of-arrival of a radio signal. Particularly, the signal is an ultra-wideband (UWB) signal. The method uses kurtosis of the received signal to estimate an energy threshold.

Brief Description of the Drawings

Figure 1 is a block diagram of a system and method for estimating an energy threshold to be used for estimating the TOA of a UWB signal according to an embodiment of the invention;

Figure 2 is a timing diagram of a received energy block of a UWB signal according to an embodiment of the invention;

Figure 3 is a graph of expected values of K with respect to Ei ₃ ZN ₀ for different block sizes;

Figure 4 is a graph of the mean absolute error of TOA estimates with respect to a normalized threshold;

Figure 5 is a graph of the optimal normalized threshold value with respect to the logarithm of kurtosis;

Figure 6 is graph of the corresponding mean absolute error for the graph in Figure 5 ; and

Figure 7 is a graph of confidence levels for ranging estimation errors.

Best Mode for Carrying Out the Invention

System Structure

Figure 1 shows a system and method 100 for determining an energy threshold ξ 101 for an ultra- wideband signal 102 according to one embodiment of the invention. The energy threshold 101 can be used for estimating 110 a time-of-arrival (TOA) 111 of a UWB signal 102 at a receiver.

The UWB signal is received at an antenna 120. The signal is preprocessed 105. During the preprocessing, the signal is low noise amplified (LNA) 130, band pass filtered (BPF) 140, squared (.) ² 150, and

integrated J 160. The resulting signal energy is sampled periodically 170 at time intervals t _s to produce samples z[n] 111.

Kurtosis analysis 180 is perfoπned on the samples. Kurtosis measures a degree of peakedness of a distribution of the real-valued random variables z[n] that are the signal samples. The kurtosis can be defined as a ratio of the second and fourth moments of the distribution of the energy in the signal samples. A distribution of normal random variables has a kurtosis of 3. A higher kurtosis means more of the variance is due to infrequent extreme deviations, as opposed to frequent modestly-sized deviations. The kurtosis is used to select 190 the energy threshold ξ 101.

System Model

The received multipath ultra-wideband (UWB) signal 102 can be expressed as

OO ^r (*) ⁼ ∑ d _ό ω _mp (t - jT _f - CjT ₀ - Ttoa) + n(t)

J=-∞ , (1 ) where j is a frame index, Tf is a frame duration, T _c is the chip duration, and τ _loa is the estimated time-of-arrival (TOA) of the received signal.

An effective pulse after the channel impulse response can be expressed by

where ω(t) is the received UWB pulse with unit energy, E _b is the symbol energy, N _s represents the number of pulses per symbol, and α/ and τ _\ are the fading coefficients and delays of multipara components, respectively.

Additive white Gaussian noise (AWGN) with zero-mean, double- sided power spectral density No/2, and variance σ ^" is denoted by n[i).

To provide processing gain, time-hopping codes C _j e {0, 1, ... , N _/ , - 1 }, and random polarity codes d _j e {±1 } are used during transmission, where N _/ , is the possible number of chip positions per frame, given by N _/ , = TfIT ₀ . We assume that a coarse acquisition, on the order of frame-length, is acquired, such that the estimated TOA is τ _toa ~ W(O, Tf), where ^Q denotes a uniform distribution.

For a search region, the signal within time frame Tf and half of the next frame is considered to include interframe leakage due to multipath interference.

The signal, after LνA 130 and BPF 140, is input to the square-law device 150 followed by integration 160 with an integration interval of 7V The integration interval determines the time- wise width of the blocks. The number of samples is denoted by Nb - 3/2(TfZTbJ, i.e., a function of frame duration and block size. The sample index is denoted by n e { 1, 2, ... , N^ }, with respect to a starting point of an uncertainty region.

With a sampling interval of t _s , which is equal to the block length T _b , the sample values 171 are given by

where the means and variances of noise-only and energy bearing blocks are given by μ ₀ = Mr ² , σ ₀ ² = 2Mσ ⁴ , μ _e = Mσ ² + E _n , σ ² = 2Mσ ⁴ + AoE _n , respectively. The degree of freedom M is given by M = 2BT _h + 1, E _n is the signal energy within the ri block, and B is the signal bandwidth. The energy of the received symbol is given by V ¹⁷ ^ ⁺ "" ^6" β _; where n _eb is the number of

blocks that sweeps the signal samples.

The received samples 171 are compared to the energy threshold 101 during the TOA estimation 110. The time index of the first sample that exceeds the energy threshold can be identified as the TOA estimate 111, i.e., i _τc = [min{φ[n] > ξ} - 0.5JT ₆ ,

^{L J} (3) where t _τc is the threshold crossing time, and ξ is the energy threshold 101 which is based on statistics of the received signal. Given samples with minimum and maximum energy, the following normalized threshold can be used

^

The norm that minimizes the mean absolute error (MAE) is defined by E\ t _τc - τ _loo j] for a particular EbINo value, where E[.] denotes an expectation operation.

However, estimation of EJNQ for a UWB signal is not trivial.

Moreover, the optimal noπnalized threshold can vary for different channel realizations with the same E _b INo.

Therefore, it is desired to improve the way that the threshold 101 is selected 190.

Threshold Selection Based on Kurtosis

In the prior art, the kurtosis has been used to estimate the SNR for conventional narrow band radio signals, Matzner et al., "SNR estimation and blind equalization (deconvolution) using the kurtosis," Proc. IEEE-IMS Workshop on Information Theory and Stats., p. 68, Oct. 1994.

Kurtosis has not been used to estimate an energy threshold of an ultra- wideband UWB signal with hundreds of multipath components. The problem is partially shown in Figure 2. In Figure 2, the blocks 201 are noise, the blocks 202 are energy blocks of the signal, and the block 203 has a peak energy. Prior art techniques typically estimate a TOA 210 based on the peak energy. This can be erroneous. A better TOA estimate would be based on the first energy block exceeding the threshold 21 1.

According to an embodiment of the invention, the kurtosis of the energy of the received signal samples z[n] 171 is determined using second and fourth order moments, and is expressed as a ratio of the fourth moment to the square of the second moment of the energy of the samples:

₍₅₎

where £{.) denotes an expectation operation.

The kurtosis relative to a Gaussian distribution can be defined as which is zero for the Gaussian distribution.

In the absence of a received signal or for a low SNR, and for sufficiently large M, the samples z\n\ 171 are Gaussian distributed, yielding K = 0. As the SNR increases, the kurtosis tends to increase, and can take different values for the same SNR value.

Figure 3 shows expected values of K with respect to E _b INQ for different block sizes over CMl and CM2 channel models according to the IEEE 802.15.4a standard, averaged over 1000 simulated channel realizations.

The system parameters are 7 _/ = 200 ns, 7 ^T _c = l ns., Z? = 4 GHz, and N ₅ =

1. The relationship shown in Figure 3 is an average relationship, and the kurtosis values for individual channel realizations can show deviations depending on the clustering of the multipath components, which also affects the optimality of the threshold for the same EbINo value.

Figure 4 shows the MAEs of the TOA estimates with respect to a normalized threshold and the kurtosis values rounded to logarithmic integers. The logarithm accounts for the clustering of the kurtosis values at low E _I JNQ. The kurtosis values are obtained for 1000 CMl simulated channel realizations with E _b /N ₀ = {10, 12, 14, 16, 18, 20, 22, 24, 26}dB. The optimal achievable MAE improves with increasing kurtosis values.

Figures 5 and 6 show the optimal normalized threshold value with respect to the logarithm of the kurtosis, and the corresponding MAE, respectively. While the channel model does not much affect the relation between ξ _opt and 1Og ₂ K ₃ the dependency changes for different block sizes. In order to model the relationship, a double exponential function fit 501 is used for T _b = 4 ns, while a linear function fit 502 is used for T _b = 1 ns.

Equations 6 and 7 can be used to select the appropriate energy thresholds. eSr ^} = 0.673e-°- ^{75 log} 2 ^κ + 0.154e-°- ^{001 lo} ^ ^κ , (6) £<£ ^* > = -0.082 \og ₂ K + 0.77 , (7)

The model coefficients are obtained from the above described relationships for both CMl and CM2 simulated data, i.e., the same coefficients are used to characterize both channel models.

It should be noted that the above technique can be used to model signals having other block sizes.

In addition, the kurtosis based energy level can also be used as a TOA application using narrow band radio signals.

It should also be noted, that the energy threshold can be used to remove noise. With reference to Figure 2, all blocks 201 with an energy level less than the threshold ξ can be removed and not be processed any further.

Effect of the Invention

With the kurtosis-based threshold selection as described above, the estimation error can be significantly decreased compared to prior art fixed threshold and SNR based techniques.

Figure 7 shows confidence level for a 3 ns estimation error, i.e., a 90 cm ranging error. If the kurtosis based normalized energy threshold is used, then a 70% confidence level can be achieved at E _b ZN ₀ larger than 22dB. Better results can be obtained via coherent ranging at lower E _b INo values.

The threshold selection method can be implemented by calibrating the system for particular block size and frame duration. The method is independent of the channel model.

Although the invention has been described by the way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the invention.

Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.