Vibration phases estimation based on multi-channel interferometry for ISAL.

High quality linear laser frequency chirp of high chirp rate is critical to many laser ranging applications. In this paper, we describe a cost-effective chirp linearization approach implemented on our Inverse synthetic Aperture LADAR (ISAL) imaging testbed. Our approach uses a COTS PZT for external cavity laser frequency tuning and a common self-heterodyne fiber interferometer as a frequency monitor, with a two-step hardware and software chirp linearization procedure to achieve high quality chirp. First, the nominal triangle waveform input to PZT drive is modified through an iterative process prior to ISAL imaging acquisition. Several waveforms with chirp rates between 1 and 4THz/s have been acquired with residual chirp rate error ~ +/-2% in usable region. This process generally needs to be done only once for a typical PZT that has excellent repeatability but poor linearity. The modified waveform is then used during ISAL imaging acquisition without active control while the imperfection in transmitted frequency is monitored. The received imaging data is resampled digitally based on frequency error calculated from the frequency monitor data, effectively reduce chirp nonlinearity to ~+/- 0.2% in chirp rate error. The measured system impulse response from return signal shows near designed range resolution of a few mm, demonstrating the effectiveness of this approach.

Inverse synthetic aperture ladar (ISAL) is analogous to the inverse synthetic aperture radar but works at frequency 104–5 times higher. A method is proposed to process the ISAL data with stepped-frequency continuous waveform (SFCW). Due to the short wavelength used for ISAL and the relatively long transmission of the SFCW, the motion during the transmission and the relative range shift between adjacent sub-band chirps must be both taken into account while performing the bandwidth synthesis and imaging. An analytic signal model was derived to address this problem. In the proposed model, three additional terms, named the bulk range walk term, the stepped phase error term and the additional range shift, were formulated in ISAL imaging with SFCW for the first time. Based on the signal model, an effective method was proposed to coherently synthesise all the sub-band echoes and then focus the ISAL image. Experimental result was shown and the focused image of an airplane model demonstrated the effectiveness of the proposed method.

During the spaceborne inverse synthetic aperture ladar (ISAL) imaging, the radial velocity between the piggyback satellite of ladar and the observed satellite is usually relatively large, which will introduce the inner-pulse Doppler that cannot be ignored in the echo. In this case, the traditional “stop-and-go” assumption will no longer hold, and the compressed result along the range direction would be seriously broaden. Therefore, to obtain a well-focused ISAL image, this article proposed a spaceborne ISAL imaging algorithm for high-speed moving targets based on the inner-pulse Doppler compensation and residue video phase (RVP) correction. In the article, the estimate method of the radial velocity, the inner-pulse Doppler compensation method, and the RVP correction method are given. After inner-pulse Doppler compensation, RVP correction and translational motion compensation, the range-compressed result under the “stop-and-go” assumption can be obtained, and the target can then be imaged by the traditional ISAL imaging algorithm. The simulation results demonstrate the effectiveness of the proposed algorithm.

Inverse synthetic aperture ladar (ISAL), which operates at visible or infrared wavelength, is more sensitive to maneuver than inverse synthetic aperture radar (ISAR). In this paper, we propose the Range Instantaneous Doppler (RID) algorithm for very high resolution ISAL imaging of maneuvering targets. The Wigner-Ville distribution (WVD) and the Radon transform are used for computing the time-varying Doppler spectrum of maneuvering targets. The data processing of the RID algorithm is illustrated. The simulation shows that the RID algorithm can reconstruct a clearer ISAL image of the maneuvering target than the conventional rang Doppler (RD) algorithm. This is a crucial step in developing a real ISAL system.

Acoustic bird species classification under low SNR and small-scale dataset conditions

In response to the application requirement of wide-range high-resolution imaging of non-cooperative moving real targets by inverse synthetic-aperture ladar (ISAL), experiments were conducted on the depolarization effect of target materials, and the polarization selection of ISAL receiving and transmitting channels was discussed. Considering the impact of target depolarization and the demand for along-track interferometry, combined with beam-broaden and high-gain amplifiers, an ISAL system design method that can stably image multiple non-cooperative real targets has been proposed. Under the condition of broadening the transmitting and receiving beams to 3° in the elevation direction for non-cooperative moving vehicles, echo data with a duration of 1 s is obtained. The spatial correlation algorithm combined with along-track interferometry is used to estimate the vibration phase error. The sub-aperture Range-Doppler algorithm is used for imaging. The ISAL imaging results of the moving vehicle validated the high-resolution imaging ability of ISAL and its potential for stable imaging of non-cooperative moving real targets.

This study proposes a bi-directional recurrent neural network (Bi-RNN) post-processing method for speech enhancement (SE) at low signal-to noise ratios (SNR). Current speech enhancement solutions performed badly under low SNR situations. Loizou and Kim proposed a solution to reduce speech distortion errors in time-frequency (T-F) domain but it requires the knowledge of ground truth. As ground truth is unknown in real-life applications, the current study proposes to use a Bi-RNN to implement Loizou and Kim’s solution as a post-processing method for SE engines. Our solutions do not require prior knowledge of ground truth. The effectiveness of the proposed method is investigated with a spectral subtraction (SS) SE engine, a non-negative matrix factorization (NMF) SE engine, and a deep neural network ideal ratio mask (DNN-IRM) SE engine, under matched/mis-matched noise and different SNR conditions. Experimental results demonstrate that the proposed post-processing method effectively improved both perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) for all of these SE engines, especially at low SNR conditions.

Generally, the performance of endpoint detection is affected by the noise. In this paper, we propose a novel two-layer decision model based on noise classification to detect the activity voice robustly. The training processing mainly contains two steps: firstly, we employ the noisex-92 database, which consists of different types of pure noise, to train a BP neural network in order to classify the noise type precisely, secondly, we train BP neural networks for each noise type covering large range of signal noise ratio (SNR). In the testing phase, we assume that the short period of silence at the beginning of the signal contains features for noise and utilize them to get the noise type. Then, we use the classifier corresponding to the noise type to detect activity voice. We conduct experiments on TIMIT corpus for 5 noise types under 7 SNR conditions. And experimental results show that our method outperforms global classifier, especially in low SNR condition.

Low signal-to-noise ratio (SNR) low probability of intercept (LPI) radar signal recognition is a key technical challenge in the field of electronic reconnaissance. Under low SNR conditions, severe noise contamination hinders the extraction of time-frequency features, resulting in degraded performance for traditional recognition methods. To address this issue, this paper proposes a cascaded framework, named MAE-WGAN, which integrates a Multi-Attention Enhanced Wasserstein Generative Adversarial Network for time-frequency image denoising, followed by radar signal recognition using a Convolutional Neural Network (CNN). The proposed approach embeds channel, spatial, and self-attention mechanisms into the U-Net-based generator of WGAN, forming a multi-stage radar signal processing system. First, under the guidance of high-quality time-frequency images, MAE-WGAN adaptively learns key time-frequency features using the multi-attention modules. Next, a discriminator is trained within the WGAN framework to recover signal structures obscured by noise. Finally, a dedicated multi-level feature extraction network is employed for accurate radar signal classification. Experimental results on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}$</tex> typical LPI radar signals demonstrate that MAE-WGAN achieves high recognition accuracy under low SNR conditions, exhibiting strong recognition performance and robustness across varying SNR conditions.

We focus on the sound source localization (SSL) problem of small/weak voices recorded by small humanoid robots, such as Nao. The localization of small voice on such robots is more difficult than those with large bodies because 1) the number of microphones and their positions are restricted and 2) the signal to noise ratio (SNR) between internal noise and human speech is low. Multiple Signal Classification (MUSIC) is a promising noise-robust SSL method, and it has several variants in its eigenvector decomposition process, such as generalized eigenvalue decomposition (GEVD-MUSIC) and generalized singular value decomposition (GSVD-MUSIC). However, their performances are seriously affected by noise properties, the number of microphone, and their configurations on each robot's body. Since these have been confirmed only on robots with large bodies and many microphones, we need to investigate which variants perform better on a small humanoid robot with internal noise in it. We furthermore propose another MUSIC based on transformed steering vector (TSV-MUSIC) as a new implementation of GEVD-MUSIC. The computational cost of TSV-MUSIC is reduced compared with GEVD-MUSIC by changing its matrix multiplication procedures. Experimental results using real recoded data showed that our TSV-MUSIC outperformed others in terms of localization correctness by about 10 points under low SNR condition. We also compared the properties and performances of MUSIC-based SSLs by using simulated data.

In this paper, a robust multiple observation likelihood ratio test (MOLRT) based voice activity detection (VAD) method is proposed. At the beginning of this paper, we introduce the Wiener filter to the observed signal in time domain which can help mitigating some noise. The reason why we use the Wiener filter for VAD is that the performance of the VAD is always better in high signal to noise ratio (SNR) range than in low SNR range. Then, some ideas are proposed to improve the performance of the MOLRT based VAD method. As we all know, a conventional MOLRT based method including three modules named likelihood ratio (LR) estimation, threshold setting and hangover technique. To improve the estimation accuracy of LR, we adopt the unbiased minimum mean-square error (MMSE) algorithm for noise power spectrum estimation in every frame, which is very effective for LR estimation in MOLRT-based VAD method. That is because the LR is a function of a prior and a posterior SNR and unbiased MMSE algorithm is very useful for noise estimation. In addition, to make our VAD method more robust, a dynamic threshold setting technique is proposed in our method, which is related to the minimum noise power spectrum. That is because minimum noise power spectrum can help us updating the value of threshold to a suitable level according to the denoised signal. Last but most important, a novel hangover algorithm is introduced in this paper comparing to the conventional HMM based hangover algorithm. In the novel hangover algorithm, the current frame is determined by the statistical result of the following speech/non-speech detections based on the likelihood ratio test. And the evaluation results reveal that proposed method significantly outperform the baseline result of LRT as regards VAD accuracy in both noise variations and low SNR conditions.

Enhanced sparse ISAR imaging by jointly using sparsity and low-rank properties

This study proposes a high precision algorithm to estimate the Enhanced Loran skywave delay. It is based on the artificial neural network. The algorithm establishes a neural network model between the receiving Enhanced Loran signal and the skywave propagation delay. By training a large number of data, the neural network model can more accurately reflect the relationship between the receiving signal and the skywave delay. This is an innovative application of the skywave delay estimation algorithm in the Enhanced Loran receiving system, especially in low signal‐to‐noise ratio (SNR) environments. The experimental results show that the accuracy of this algorithm is about hundreds of nanoseconds in the condition of normal receiving SNR. The accuracy of the algorithm is about us in the condition of low SNR, while the previous algorithm cannot be used in this case. It has also been proved by the off‐air data.

Recent progress in bistatic radar techniques can be used to improve performances of classical monostatic radar. A prominent limitation of coherent radar is its inability to measure the complete velocity vector (magnitude and direction) of a detected target. A single coherent detection can provide range-rate only. At least two detections, separated in time, are needed to estimate the target's velocity vector. This study discusses how the velocity vector can be determined by two simultaneous detections spaced in distance. The second detection is obtained by an auxiliary distant bistatic coherent receiver; an approach proposed in the 1990s to enhance meteorological radar. Being a very simple case of a distributed radar system allows for a simple demonstration of how to calculate the target's position and velocity vector and how to analyse the estimation accuracy, including geometric dilution of precision plots of the velocity error. Also discussed are two methods to identify correct data association when more than one target is detected.

Regulation of drones is already an important research topic. The drone identification method based on the radio frequency (RF) signal analysis technology is an efficient approach to regulate mainstream civilian drones. However, existing research on drone identification has been conducted under high signal-to-noise ratio (SNR) conditions. In fact, long-range drone identification in urban environments is performed under low SNR conditions. In this article, a global context (GC)-based threshold strategy is proposed to solve the above problem. The threshold is automatically determined during the deep architecture optimization. Meanwhile, the unimportant features in the network are removed by threshold denoising. First, the weight of each feature channel is obtained by modeling cross-channel dependencies, and then a weighted average is performed for each position along the feature channel on the feature map to obtain the thresholds. The method reduces the impact of unimportant features on performance without complex data preprocessing. In the experiments, the performance and complexity of the proposed method are extensively evaluated by 11 different drone RF signals. The experimental results show that in comparison with other deep learning-based threshold calculation methods, the proposed method has a more outstanding performance and lower computational complexity for drone RF signal identification under the low SNR condition.