分布式MIMO雷达信号级量化融合检测方法研究

针对分布式多输入多输出雷达信号级融合检测的通信带宽过大的问题,提出了一种信号级量化融合检测算法。文中首先对量化融合检测算法进行了理论推导,给出了量化后融合中心的判决准则;然后,以最大化巴氏距离为目标构建了量化准则,并且给出了求解量化门限的迭代步骤。仿真结果表明:该方法在将通信带宽降低一半的情况下,依然能够获得与信号级融合检测算法相同的检测性能。     

Asymptotically optimal block quantization

In 1948 W. R. Bennett used a companding model for nonuniform quantization and proposed the formulaD : = : frac{1}{12N^{2}} : int : p(x) [ É(x) ]^{-2} dxfor the mean-square quantizing error whereNis the number of levels,p(x) is the probability density of the input, andE prime(x) is the slope of the compressor curve. The formula, an approximation based on the assumption that the number of levels is large and overload distortion is negligible, is a useful tool for analytical studies of quantization. This paper gives a heuristic argument generalizing Bennett's formula to block quantization where a vector of random variables is quantized. The approach is again based on the asymptotic situation whereN, the number of quantized output vectors, is very large. Using the resulting heuristic formula, an optimization is performed leading to an expression for the minimum quantizing noise attainable for any block quantizer of a given block sizek. The results are consistent with Zador's results and specialize to known results for the one- and two-dimensional cases and for the case of infinite block length(k rightarrow infty). The same heuristic approach also gives an alternate derivation of a bound of Elias for multidimensional quantization. Our approach leads to a rigorous method for obtaining upper bounds on the minimum distortion for block quantizers. In particular, fork = 3we give a tight upper bound that may in fact be exact. The idea of representing a block quantizer by a block "compressor" mapping followed with an optimal quantizer for uniformly distributed random vectors is also explored. It is not always possible to represent an optimal quantizer with this block companding model.     

Encoding of Speech Spectral Parameters Using Adaptive Quantization Range Method

Efficient quantization methods of the line spectrum pairs (LSP) which have good performances, low complexity and memory are proposed. The adaptive quantization range method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector‐scalar hybrid quantizer. As the maximum quantization range of each LSP parameter is varied adaptively on the quantized value of the previous order's LSP parameter, efficient quantization methods can be obtained. The proposed scalar quantization algorithm needs 31 bits/frame, which is 3 bits less per frame than in the conventional scalar quantization method with interframe prediction to maintain the transparent quality of speech. The improved vector‐scalar quantizer achieves an average spectral distortion of 1 dB using 26 bits/frame. The performances of proposed quantization methods are also evaluated in the transmission errors.     

宽带ISF参数的非等系数帧间预测分裂矢量量化方法

 本文提出了一种新的适用于宽带语音编码ISF参数量化的非等系数帧间预测分裂矢量量化方案.该量化方案利用ISF参数的帧间相关性,基于预测分裂矢量量化原理,首先对待量化的ISF参数矢量进行去均值和非等系数帧间预测,然后对去均值后的ISF参数的预测残差进行分裂矢量量化.实验表明,该算法在每帧编码比特数为46bits时达到了透明量化,且平均谱失真比G.722.2中ISF参数量化的平均谱失真小.     

基于均匀网格编码量化的超光谱图像自适应压缩

提出一种基于小波系数分类的超光谱图像压缩方法．算法首先将各波段小波分解并将所得子带划分成子块,而后根据子块活动性将其分类．在分类基础上,使用预测差分技术去除谱间冗余,此过程中分别求取各子类的预测系数以反映子带的局部相关性,而后利用均匀网格编码量化方法来量化残差系数序列,最后使用自适应算术编码对量化码字进行熵编码,为使编码器能在所有系数序列中最优地分配比特,本文提出一个基于序列统计特性和网格编码量化器率-失真特性的比特分配算法,实验证明该方法能高效地压缩超光谱图像,表现出优异的压缩性能。     

宽带ISF参数的转换分类乘积码锥形矢量量化

本文提出了一种新的应用于宽带导抗谱频率参数量化的转换分类乘积码锥形矢量量化方案.该量化器基于转换分类与乘积码锥形矢量量化原理,首先对待量化的ISF参数矢量进行分类,然后按类进行乘积码锥形矢量量化.该算法具有低存储量及低复杂度的特点.实验表明,该算法在每帧编码比特数为46时,平均谱失真比乘积码锥形矢量量化低,且达到了透明量化标准.     

Diversity Cutoff Rate Evaluation of the Rayleigh and Rician Fading Channels

This paper presents expressions for the cutoff rate R0diversity transmission over the Rayleigh and Rician fading channels withM-ary orthogonal signaling. These expressions include the unquantized R0forD-fold diversity, which upper bounds the channel performance, and hard decision and four- and eight-level soft decision quantized R0expressions. Tradeoffs betweenDand the number of quantization levels for equivalent performance are presented for the unquantized and quantized channels. These tradeoffs illustrate the reduction in signal energy, system bandwidth, and system complexity by increasing the number of quantization levels, thereby allowing a reduction inD.     

Design of Semi-Uniform Quantizers and Their Application in Sigma Delta A/D Converters

In this paper a new type of non-uniform quantizer, semi-uniform quantizer, is introduced. A k-bit semi-uniform quantizer uses the thresholds defined by a (k + 1)-bit uniform quantizer and arranges them in such a way that small-amplitude inputs will be quantized by small quantization steps and large-amplitude inputs by large quantization steps. Therefore the total quantization error power could be reduced and the modulator's dynamic range could be increased by 1-bit. The condition for a semi-uniform quantizer to achieve a better performance than a uniform quantizer is analyzed and verified using a second order 3-bit sigma delta modulator prototype chip, fabricated in 0.35 μm CMOS process. At 32× oversampling ratio the modulator achieves 81 dB dynamic range and 63.8 dB peak SNDR with 3-bit semi-uniform quantizer. With 3-bit uniform quantizer the dynamic range is 70 dB and the peak SNDR is 54.1 dB.     

Turbo decoding of quantized data

Much of the work on turbo decoding assumes that the decoder has access to infinitely soft (unquantized) channel data. In practice, however, a quantizer is used at the receiver and the turbo decoder must operate on finite precision, quantized data. Hence, the maximum a posteriori (MAP) component decoder which was designed assuming infinitely soft data is not necessarily optimum when operating on quantized data. We modify the well-known normalized MAP algorithm taking into account the presence of the quantizer. This algorithm is optimum given any quantizer and is no more complex than quantized implementations of the MAP algorithm derived based on unquantized data. Simulation results on an additive white Gaussian noise channel show that, even with four bits of quantization, the new algorithm based on quantized data achieves a performance practically equal to the MAP algorithm operating on infinite precision data     

基于压缩感知的线谱对参数降维量化算法

为实现高质量的极低速语音编码,提出一种基于压缩感知理论的线谱对(LSP)参数降维量化算法。编码端利用压缩感知理论对超帧LSP高维矢量进行降维处理,将原始LSP参数投影到低维空间,得到低维测量值,然后采用分裂矢量量化算法对测量值进行量化;解码端以量化后的测量值为已知条件,利用正交匹配追踪算法重构出原始LSP高维矢量。实验结果表明,本算法相对低速语音编码中的矩阵量化方案,平均谱失真降低了0.23dB,相对基于DCT变换的降维量化方案,平均谱失真降低了0.13dB。这种先降维再量化的思想可以大幅减少编码所需的比特数及码本存储复杂度,有效降低语音编码速率,并且合成语音可懂度、自然度较高,音质虽有所失真,但基本上感觉不到明显的听觉质量下降。      

Cooperative Spectrum Sensing with Adaptive Double-Threshold Based Energy Detector in Cognitive Radio Networks

Spectrum sensing is one of the key functionalities in the implementation of cognitive radio. It is used to sense the unused spectrum in an opportunistic manner. In this paper, we propose an energy detector with adaptive double-threshold for spectrum sensing, to optimize the detection performance at a fixed probability of false alarm $(\text{ P }_\mathrm{f})$ i.e. 0.1, which also overcomes sensing failure problem. In the present work, the detection threshold is made adaptive to the fluctuation of the received signal power in each local detector of cognitive radio (CR) user. Simulation results show that proposed scheme optimizes better detection performance and outperforms both conventional energy detector and cooperative spectrum sensing (CSS) method by 12.8 and 3.3 % at $-$ 8 dB signal to noise ratio (SNR), respectively. While utilizing CSS with proposed adaptive double-threshold scheme, where each CR user use a double threshold detectors for local detection and send detection decisions to fusion center (FC) to give the final decision based on hard decision rule. It is further found that CSS with adaptive double-threshold improves detection performance around 26.8 and 7.6 % as compare to CSS with single threshold and Hierarchical with quantization method at $-$ 10 dB SNR, respectively, under the case when a small number of sensing nodes are used in spectrum sensing.     

一种适用于低速率语音编码系统的基音周期量化算法

本文提出了一种适用于低速率语音编码系统的基音周期量化算法。该量化器由一个无记忆均匀量化器和一个差分量化器组成。其中,一个量化器直接量化对数基音周期,另外一个量化器对当前的待量化的对数基音周期与前一个已量化的对数基音周期的差分值进行量化。两个量化器的输出结果择优传送到解码器。该方法利用语音信号中的冗余特性和人耳的听觉特性获得了一个高效的量化算法。实验结果表明,该算法对基音周期的量化只用4比特而不降低合成信号的听觉质量。     

A time-domain noise-coupling technique for continuous-time sigma-delta modulators

In this paper, a time-domain noise-coupling technique based on the pulse width modulation is proposed. The time-domain quantization error is digitally extracted and shaped by an asynchronous digital filter. This digitally filtered quantization error is applied to the quantizer input to increase the modulator’s noise-shaping order. By using this technique in continuous-time sigma-delta modulators, the modulator’s shaping property is significantly enhanced. Comparative analytical calculations and simulation results are presented to estimate the performance of modulators employing the proposed quantizer. System-level simulation results reveal a (L + 2)th order noise-shaping capability of the proposed modulator while it employs only L analog integrators. The effects of main circuit non-idealities in the modulator’s performance are analytically investigated and confirmed by the simulation results.     

时间相关信道的量化反馈算法研究

针对实际通信信道的时间相关特性,提出了一种基于有限状态向量量化（FSVQ）的信道量化算法。算法首先将信道划分为有限个状态,并为每一状态设计一个码本,码本用来量化从对应状态转移而来的信道。接收机只需要反馈信道向量在状态码本中的量化码字的序号。跟不考虑时间相关的向量量化算法相比,该算法能以相同的反馈负荷获得更高的性能。仿真结果表明,当信道相关系数为0.9,发射天线为6个,发射信噪比为0 dB的时候,该算法能提高接收信噪比性能1 dB。     

Optimum Detection of Quantized PAM Data Signals

The degree of complexity of a digital signal processor is closely related to the precision with which samples of an incoming analog waveform are represented. There is considerable interest in determining how coarse this representation can be without seriously degrading performance from that of an ideal processor of unquantized samples. This question is examined for a receiver of noisy, linearly distorted pulse amplitude modulation (PAM) signals. An optimum [maximum likelihood (ML)] detector, analogous to the Viterbi detector for unquantized samples, is derived for the case of a quantized sample sequence. Performance is evaluated under the assumption of high signal-to-noise ratio (SNR), and the resultant error probability is a good approximation for coarse quantization, and an upper bound for any degree of quantization. For a specified error probability, the degree of quantization suggested by this approach is conservative. Since receiver complexity is closely associated with the length of the digital representation of an input sample, an upper bound on receiver complexity is also suggested. Numerical evaluation of the error probability is quite tedious for an arbitrary channel; however, system performance may be readily evaluated for partial-response (PR) signaling. For the PR channels     

Fixed-Tap ADPCM System Divergence and a Bound on the Robust Quantizer Overload Point

The divergence of ADPCM systems with fixed, multipletap predictors and a Jayant quantizer is investigated. It is shown that system divergence occurs due to excessive quantization noise in the feedback loop coupled with the infinite quantizer memory. Further, divergence may result for even finer quantization if the predictor is poorly matched with the system input. New insight into quantizer/ predictor interaction is provided by a demonstration that for all average speech data available in the literature and more than one feedback tap, the system that describes the quantization noise evolution is unstable whenever the predictor is stable. It is noted that robust quantizer designs originally proposed for transmission error suppression are also effective in preventing the ADPCM system divergence problem discussed here, and a bound on the robust quantizer overload point is derived which illustrates the effect of the finite quantizer memory. Simulation results which validate the bound are presented.     

Position‐based adaptive quantization for target location estimation in wireless sensor networks using one‐bit data

The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one‐bit information to its fusion center. To improve the performance of estimation, a position‐based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position‐based information sequences and then sends its one‐bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position‐based maximum likelihood estimator as well as its corresponding position‐based Cramér–Rao lower bound are derived. Numerical results show that the position‐based maximum likelihood estimator is more accurate than the classical fixed‐quantization maximum likelihood estimator and the position‐based Cramér–Rao lower bound is less than its fixed‐quantization Cramér‐Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.     

基于性能边界和量化数据的WSN目标跟踪传感器选择算法

对能量和带宽受限的无线传感器网络下的目标跟踪问题,基于量化的观测数据和条件后验克拉美-罗下界提出一种传感器选择方法.为了节约网络能量和带宽,对传感器接收到的观测数据进行量化压缩,推导了传感器量化数据下目标状态估计的条件后验克拉美-罗下界,将其作为传感器选择和优化的准则,并且利用粒子滤波器给出一种条件后验克拉美-罗下界的近似计算方法.与基于无条件后验克拉美-罗下界和互信息的传感器选择方法进行了对比仿真,结果表明了条件后验克拉美-罗下界作为传感器选择准则的有效性以及对跟踪性能的改进.     

Novel quantization‐based spectrum sensing scheme under imperfect reporting channel and false reports

In this paper, a novel sensing scheme, uniform quantization for cooperative sensing (UniQCS), that employs a uniform quantizer is proposed. UniQCS is based on energy detection and uses weight vector for global decision function. It performs better than hard decision algorithms such as majority and k‐out‐of‐n in terms of probability of detection and false alarm at the cost of a marginal increase in overhead bits under imperfect reporting channel and false reports. The probability of detection is maximized for a given probability of false alarm constraint by the proposed method. For detailed analysis, the UniQCS is compared with equal gain combiner scheme, which performs far better than hard decision algorithms, via highest bandwidth requirement. The proposed algorithm performs close to equal gain combiner. Moreover, the robustness of UniQCS to sensing error is analyzed when some nodes always report false decisions to the fusion center and the reporting channel is imperfect. For probability of false alarm equal to 0.01, performance gain of UniQCS is at least 45% compared with the other methods when there are two false reporting nodes. UniQCS performance gain is at least 15% compared with other methods for probability of reporting channel error equal to 0.001. Copyright © 2012 John Wiley & Sons, Ltd.     

Study of an adaptive quantizer

An adjustable uniform quantizer dependent on observation of blocks of quantized samples is investigated. In this quantizer, the range the samples are expected to occupy within a block is predicted from observation of the previous block. Then the upper and lower saturation levels of the quantizer are adjusted independently to correspond to this predicted range. This procedure is repeated for each new observed block of samples. The adaptive quantizer is evaluated by means of a computer simulation, comparing it to a uniform quantizer with fixed saturation levels. The system is evaluated for television signals, spacecraft engineering sensor signals, and a multiple Gaussian Markov process. For the television signals, the adaptive quantizer acquires a "variable-range" mode of operation, making use of coherence between successive lines in a frame to achieve a reduction in error. When processing the other signals, the adaptive quantizer acquires a "fixed-range variable-mean" mode of operation achieving reductions in mean squared quantization error from 30 to 90 percent. A comparison with an ideal quantizer illustrates the ability of the adaptive quantizer to make effective use of coherence between samples to achieve a reduction in quantization error.