Method for determining of the distance to the object in the hyperbolic navigation system via doppler frequency offsets

In this paper, a method for estimating the distance to the object guided along a hyperbola to a target using a bistatic hyperbolic navigation system on a plane is given. At the same time, to solve the guidance problem, the number of required navigation positions is reduced by one in comparison with the classical method of hyperbolic navigation. However, in the guidance algorithms, it is still required to estimate the distance of the targeted object from the center of the base, the methods of obtaining which are considered in the work.

Spatial Transformer Network-Based Automatic Modulation Recognition of Blind Signals

Modulation recognition of communication signals plays an important role in both civil and military uses. Neural network-based modulation recognition methods can extract high-level abstract features which can be adopted for classification of modulation types. Compared with traditional recognition methods based on manually defined features, they have the advantage of higher recognition rate. However, in actual modulation recognition scenarios, due to inaccurate estimation of receiving parameters and other reasons, the input signal samples for modulation recognition may have large phase, frequency offsets, and time scale changes. Existing deep learning-based modulation recognition methods have not considered the influences brought by the above issues, thus resulting in a decreased recognition rate. A modulation recognition method based on the spatial transformation network is proposed in this paper. In the proposed network, some prior models for synchronization in communication are introduced, and the priori models are realized through the spatial transformation subnetwork, so as to reduce the influence of phase, frequency offsets, and time scale differences. Experiments on simulated datasets prove that compared with the traditional CNN, ResNet, and the CLDNN, the recognition rate of the proposed method has increased by 8.0%, 5.8%, and 4.6%, respectively, when the signal-to-noise ratio is greater than 0. Moreover, the proposed network is also easier to train. The training time required for convergence has reduced by 4.5% and 80.7% compared to the ResNet and CLDNN, respectively.

Beampattern analysis of frequency diverse array radar: a review

Electronic beam steering is an essential feature of state-of-the-art radar systems. Conventional phased array (PA) radars with fixed carrier frequencies are well-known for electronically steering their beam with high directivity. However, the resulting beampattern is angle-dependent but range-independent. Recently, a new electronic beam steering concept, referred to as frequency diverse array (FDA) radar, has attracted increasing attention due to its unique range-angle dependent beampattern. More importantly, the FDA radar employs a small frequency increment across the array elements to achieve beam steering as a function of angle, range, and time. In this paper, we review the development of the FDA radar since its inception in 2006. Since the frequency offset attaches great importance in FDAs to determine the beampattern shape, initially much of the research and development were focused on designing the optimal frequency offsets for improved beampattern synthesis. Specifically, we analyze characteristics of the FDA beampattern synthesis using various frequency offsets. In addition to analyzing the FDA beampattern characteristics, this study also focuses on the neglected propagation process of the transmitted signals in the early FDA literature, and discuss the time-variant perspective of FDA beampatterns. Furthermore, FDA can also play a significant role in wireless communications, owing to its potential advantages over the conventional PAs. Therefore, we highlight its potential applications in wireless communication systems. Numerical simulations are implemented to illustrate the FDA beampattern characteristics with various frequency offset functions.

Accurate Signal Recovery in UHF Band Reuse-1 Cellular OFDMA Downlinks

<pre>Accurate signal recovery is challenging for non-co-located transmit antennae deployments due to Inter Tower Interference (ITI) in reuse-1 cellular OFDMA networks. In the sub-1 GHz UHF band where only SISO deployment is possible, interference aware receiver algorithms are essential to mitigate the ITI. In this work, we develop a Joint Modified Least Squares (JmLS) algorithm for channel estimation in the presence of ITI. Firstly, it is shown that the JmLS algorithm achieves the Cramer-Rao lower bound. Next, an approach to managing the possibly distinct carrier frequency offsets of the different co-channel signals of interest is proposed. This improves the quality of the bit-level Joint Log-Likelihood Ratio. Finally, the impact of the choice of pilot sub-carrier information in the block modulated air-interface on the coded block error rate performance is studied. In particular, a comparison is made between (i) frequency orthogonal pilots from the different sectors, vis-a-vis, (ii) a pilot-on-pilot arrangement using pseudo-orthogonal sequences. The study indicates that based on the extent of frequency selectivity and the number of interferers being considered, (ii) is advantageous when the set of ITI pilots incident on a receiver is small when compared to the set of all possible pilots.</pre>

Accurate Signal Recovery in UHF Band Reuse-1 Cellular OFDMA Downlinks

<pre>Accurate signal recovery is challenging for non-co-located transmit antennae deployments due to Inter Tower Interference (ITI) in reuse-1 cellular OFDMA networks. In the sub-1 GHz UHF band where only SISO deployment is possible, interference aware receiver algorithms are essential to mitigate the ITI. In this work, we develop a Joint Modified Least Squares (JmLS) algorithm for channel estimation in the presence of ITI. Firstly, it is shown that the JmLS algorithm achieves the Cramer-Rao lower bound. Next, an approach to managing the possibly distinct carrier frequency offsets of the different co-channel signals of interest is proposed. This improves the quality of the bit-level Joint Log-Likelihood Ratio. Finally, the impact of the choice of pilot sub-carrier information in the block modulated air-interface on the coded block error rate performance is studied. In particular, a comparison is made between (i) frequency orthogonal pilots from the different sectors, vis-a-vis, (ii) a pilot-on-pilot arrangement using pseudo-orthogonal sequences. The study indicates that based on the extent of frequency selectivity and the number of interferers being considered, (ii) is advantageous when the set of ITI pilots incident on a receiver is small when compared to the set of all possible pilots.</pre>

Kurtosis-Based Symbol Timing and Carrier Phase/Frequency Tracking

Kurtosis is known to be effective at estimating signal timing and carrier phase offset when the processing is performed in a “burst mode,” that is, operating on a block of received signal in an offline fashion. In this paper, kurtosis-based estimation is extended to provide tracking of timing and carrier phase, and frequency offsets. The algorithm is compared with conventional PLL-type timing/phase estimation and shown to be superior in terms of speed of convergence, with comparable variance in the matched filter output symbols.

Joint Timing and Frequency Synchronization Using Convolutional Neural Network in WLAN Systems

In wireless communication systems, the performance of the receiver is very sensitive to time and frequency offsets. In particular, orthogonal frequency division multiplexing (OFDM) systems are highly vulnerable to those offsets due to inter-carrier interference (ICI) and inter-symbol interference (ISI). To solve this problem, wireless local area network (WLAN) systems transmit a preamble for synchronization. In this paper, we propose a joint time and frequency offsets estimation technique based on convolutional neural network (CNN) for WLAN systems. In the proposed technique, the correlation between the received signal and the transmitted preamble is performed first. Then the frequency offset is coarsely compensated by several hypothesized offsets. The compensated signals are inputted to the proposed CNN and the CNN predicts the time and frequency offsets. The estimation performance is examined through computer simulation. According to the results, the proposed time offset estimator shows 3 dB to 6 dB performance gain, and the frequency offset estimator shows much lower root mean square error (RMSE) performance than the conventional technique at low SNRs