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As shown in Fig. 1, in the traditional energy detection methods, the received signal is squared by the low noise amplifier, and then input into an integrator with period T b which is much longer than the sampling interval, so the number of energy blocks within a frame is N b = ⌊T f /T b ⌋ (⌊⌋denotes the integer part) and T f is the frame period.
In parametric PSD estimation methods, the received signal samples are modeled as the output of a linear filter to white noise.
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With the CAPC-1 method, the received SNR is too low during a deep fade.
Thus, threshold crossing method has been proposed; with this method, the received energy blocks z[n] are compared with a selected threshold.
In this method, the received signal is decomposed by "db6" wavelet and the kurtosis of energy blocks of the low-frequency wavelet coefficients (K c) is analyzed.
In the case of two nodes beamforming using the proposed method, the received signal power achieves 92% of its ideal value, which is 5.3 dB higher compared with signal node transmission.
Based on the operator method, the received results of this paper are analogues of Le Cam's inequality in classical literature (we refer the reader to Steele [7], Le Cam [5], Chen [8], Neammanee [9], and Wang [3] for a complete treatment of the problem).
The system offers better localization accuracy than other fingerprint-based methods, such as methods employing the received signal strength fingerprint with kernel distance [8] and the decimal-scale ACIR fingerprint with general regression neural networks generalized from [9].
However, most of these methods use the received signal strength indicator (RSSI) as data for location determination [7, 8].
In the PHC method, all the received words are classified into a code word or a 1-bit error.
The CAPC-3 and MFxLMS methods can keep the received signal close to the desired value.
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