Acoustic Doppler Current Profilers (ADCP) are typical equipment for underwater velocity measurement using layered echo signal processing. However, the aliasing effect influences the measurement accuracy, which is rela...
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Acoustic Doppler Current Profilers (ADCP) are typical equipment for underwater velocity measurement using layered echo signal processing. However, the aliasing effect influences the measurement accuracy, which is related to the auto-correlation function of the echo signal. To this end, a novel complementary coding-based waveform is proposed in this article. First, we establish a current velocity measurement framework for broadband ADCP (BBADCP), consisting of a complex auto-correlation algorithm and an ambiguity-resolving algorithm. Second, we find that the waveform design criteria are proportional to or inverse proportion to the symbol length, symbols repetition times, and bit-width, resulting in mutual constraints of each other. Thus a parameter compromise scheme is proposed to solve the mutual constraints among various metrics, including depth cell thickness, velocity accuracy, and maximum measurable velocity. Third, we propose an aliasing effect count-measure method by transmitting two repeat coding pulses using a pair of complementary codes with a time delay. We also offer a concatenation method to extend the complementary codes, which improves the velocity estimation precision without affecting the range resolution. Simulation results demonstrate that the waveform embedding extended complementary codes provides advantages in both velocity estimation accuracy and maximum measurable velocity.
Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have sho...
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Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall ordinary performance. We propose an integrated radar and jamming waveform based on smart modulation and complementary coding. Unlike traditional integrated radar and jamming waveform based on smart modulation, the phase angle of the binary phase-coded sequence is adjustable in this smart modulation method, allowing for a controllable jamming effect, achieving true smart modulation. However, this smart modulation waveform also suffers from high sidelobes in detection. To address this issue, we take a complementary coding approach and design a smart modulation waveform with complementary characteristics. This waveform can synthesize a complete linear frequency modulation (LFM) signal by adding two pulses together, thereby reducing the sidelobes in the smart modulation waveform and enhancing its detection performance. Theoretical analysis indicates that the detection and jamming effects of this integrated waveform can be flexibly controlled by adjusting the phase angles of the coding sequences. Simulation analysis and experimental results confirm the significant advantages of this waveform.
We have implemented a BOTDA that combines channel estimation and complementary coding technique for high performance distributed sensing. The use of OFDM-modulated probe reduces the measurement time sharply compared t...
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We have implemented a BOTDA that combines channel estimation and complementary coding technique for high performance distributed sensing. The use of OFDM-modulated probe reduces the measurement time sharply compared to the classical frequency sweeping technique. Meanwhile, the complementary coding provides a higher SNR of the sensing system, which further reduces the measurement time by mitigating averaging. The method of combining the channel estimation technique and complementary coding is theoretically analyzed. It has been demonstrated that the pulse coding scheme can benefit both the Brillouin gain spectrum (BGS) and Brillouin phase spectrum (BPS). Experiments are carried out using 512-bit codewords on a 10-km single-mode fiber with a spatial resolution of 25 m. The complex BGS distribution is measured within 2 ms, with a BFS uncertainty of 1.1 and 0.6 MHz for BGS and BPS, respectively.
The notion of complementary coding is generally thought to be a waveform design problem. In contrast, here we show that, for arbitrary diverse FM waveforms over the coherent processing interval (CPI), least-squares mi...
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ISBN:
(纸本)9781728126609
The notion of complementary coding is generally thought to be a waveform design problem. In contrast, here we show that, for arbitrary diverse FM waveforms over the coherent processing interval (CPI), least-squares mismatched filters can be jointly computed to provide complementary sidelobe cancellation on receive when pre-summing of unique waveform subsets is performed after pulse compression (before Doppler processing). The efficacy of this scheme is demonstrated in simulation and with experimental free-space measurements.
High frequency (HF) band covers frequencies between 3-30 MHz. With wavelengths of 10-100 m, HF band enables communications over a very long distances though a skywave propagation. As HF band has several benefits like ...
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ISBN:
(纸本)9781467399913
High frequency (HF) band covers frequencies between 3-30 MHz. With wavelengths of 10-100 m, HF band enables communications over a very long distances though a skywave propagation. As HF band has several benefits like versatility and large coverage area, it suffers phenomena like rapidly changing environment and strong interference. The cognitive radio concept which adapts to the changes in the environment offers tools to solve those problems. In this paper, HF band challenges caused by ionospheric break and interference are solved using databases and complementary codes that adapt to the characteristics of different data transmission requirements. The goal is to optimize used frequencies based on the data transmission needs.
A fuzzy neural network, Falcon-MART, is proposed in this paper. This is a modification of the original Falcon-ART architecture. Both Falcon-ART and Falcon-MART are fuzzy neural networks that can be used as fuzzy contr...
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A fuzzy neural network, Falcon-MART, is proposed in this paper. This is a modification of the original Falcon-ART architecture. Both Falcon-ART and Falcon-MART are fuzzy neural networks that can be used as fuzzy controllers or applied to areas such as forgery detection, pattern recognition and data analysis. They constitute a group of hybrid systems that incorporate fuzzy logic into neural networks. In this way, the structure of these hybrid networks become transparent as high level IF-THEN human-like reasoning is used to interpret the network connections. In addition, the hybrid networks automatically derive the fuzzy rules (knowledge base) of the problem domain using neural network techniques and hence avoid the pitfalls of traditional fuzzy systems. The main problem in designing a fuzzy neural network is how to formulate the fuzzy rule base. Most proposed fuzzy neural networks in the literature could be classified into two categories. The first group assumes the existence of a preliminary rule base and uses neural techniques to tune the parameters to obtain the final set of fuzzy rules. The second group assumes no knowledge of any fuzzy rules and performs a cluster analysis on the numerical training data before formulating the rules from the computed clusters. Falcon-ART attempts to overcome the constraints faced by these two groups of fuzzy neural networks by using the fuzzy ART technique to partition the training data set. However, there are several shortcomings in the Falcon-ART network. They are: 1. Poor network performances when the classes of input data are closely similar to each other;2. Weak resistance to noisy/spurious training data;3. Termination of network training process depends heavily on a preset error parameter;and 4. Learning efficiency may deteriorate as a result of using complementary coded training data. Falcon-MART has been developed to address these shortcomings. To evaluate the effectiveness of Falcon-MART, three different sets of experime
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