Performance Of Matched Filter Research Articles

HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

Random access schemes are widely used in IoT wireless access networks. They enable a reduced complexity and overcome power consumption constraints. Nevertheless, random access results in high packet losses which are caused by overlapping transmissions. Signal detection methods for digital modulation techniques are typically based on the well-established <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matched filter</i> , which is proven as the optimal filter under additive white Gaussian noise for minimizing error probability. However, with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">colored</i> interference arising from the overlapping IoT transmissions, deep learning approaches are being considered a suitable alternative. In this paper, we present a hybrid framework, dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HybNet</i> , that alternates between deep learning and match filter pathways based on the perceived interference level. This helps the detector to work in a broader range of conditions, optimally leveraging both the matched filter and deep learning advantages. We compare the performance of several possible data modalities and detection architectures concerning the interference-to-noise ratio, demonstrating that leveraging domain knowledge by pre-processing the input data in conjunction with the proposed HybNet surpasses the complex conjugate matched filter performance under interference-limited scenarios.

Robust Adaptive Pulse Compression Method Based on Two-Stage Phase Compensation

The conventional pulse compression method [i.e., matched filter (MF)] and adaptive pulse compression (APC) methods are based on the assumption that the echo sampling point is located in the target point which is in a range cell. If the echo sampling point is not located in the target point, it will result in sampling mismatch problem and the performance of MF and APC methods will be significantly reduced when using the continuous-time phase-modulated waveforms, such as linear frequency modulation (LFM) signal. Aiming at solving the sampling mismatch problem, an APC based on two-stage phase compensation (TPC-APC) and its dimensionality-reduced version-contiguous fast APC based on two-stage phase compensation (TPC-CFAPC) are proposed in this article. In TPC-APC/TPC-CFAPC method, the mismatch phase caused by the sampling mismatch is first compensated through the first-stage phase compensation, which suppresses the sampling-mismatch-induced range sidelobes; then the mismatch phase caused by the target’s Doppler frequency is compensated through the second-stage phase compensation to suppress the Doppler-mismatch-induced range sidelobes; finally, the APC is applied to suppress the range sidelobe. To further compensate the mismatch phases of different targets, this article proposes a reiterative TPC-APC method and its dimensionality-reduced version, which iteratively use TPC-APC or TPC-CFAPC to suppress the range sidelobes caused by the mismatch phases of different targets. The results of the experiments have shown that the proposed methods are more robust compared with MF and APC methods.

A Phase-Coded Sequence Design Method for Active Sonar.

Phase-coded sequences are widely studied as the transmitted signals of active sonars. Recently, several design methods have been developed to generate phased-coded sequences satisfying specific aperiodic or periodic autocorrelation sidelobe level metrics. In this paper, based on the majorization–minimization strategy and the squared iterative acceleration scheme, we propose a method to generate sequences with the periodic weighted integrated sidelobe level metric. Numerical simulations illustrate that the proposed method can effectively suppress the periodic autocorrelation sidelobe levels in specific time lags. Compared with other sequence design methods satisfying the periodic weighted integrated sidelobe level metric, our method improves the computational efficiency significantly. In addition, the proposed sequence demonstrates better matched filter performance in specific range intervals compared with its counterpart. The results suggest that the method could be applied as a valid and real-time design method for transmitted signals of active sonars.

Inherent limitation of digital imagery: spatial-phase vacillations and the ambiguity function

The spatial relationship of a scene image upon a pixilated focal-plane-array can temporally change due to scene or camera motion, microjitter of camera line-of-sight, etc. It was found that degradation caused by subpixel spatial-phase vacillations (SPV) upon the performance of matched filters was unexpectedly significant. Subpixel spatial-phase vacillations can cause degradations in matched filter performance to detect desired objects and discriminate other objects. SPV appear to be an inherent limitation of digital imagery when processed using matched filter methodology and can negatively impact the performance of systems. Mitigation of this degradation was found to be possible by utilizing one of several matched filter constructions such as a multi-filter enhanced matched filter (EMF) bank and a single-filter EMF. A significant conclusion of this investigation is that, for automatic target recognition applications, improved overall task performance should be realized by the use of an EMF bank. Dramatic reduction in computational resources for image matching multispectral imagery was accomplished by spectrally-collapsing multispectral imagery to form pseudoimages with the spectral information appearing as a texture in a grayscale image. The pattern recognition ambiguity function, which sets the fundamental performance limit of an image processing system, is introduced.

A Pragmatic Approach to Massive MIMO for Broadband Communication Satellites

In this paper, we analyze the performance of Massive Multiple Input Multiple Output (M-MIMO) techniques aiming at increasing the throughput of broadband satellites. In particular, we investigate a “pragmatic” approach to the design of the M-MIMO to ease its implementation both at system and satellite payload level. We compare the performance of optimized classical Minimum Mean Square Error (MMSE), Zero Forcing (ZF), Matched Filter (MF) schemes with the proposed “pragmatic” M-MIMO one dubbed fixed Multi-Beam (MB). To further boost the M-MIMO performance, a novel radio resource management approach based on Mixed Integer Quadratic Programming (MIQDP-RRM) is proposed. The adoption of MIQDP-RRM is shown to greatly enhance the M-MIMO throughput performance. It is shown that the MB scheme closely approximate the MF, ZF and MMSE performance with a much simpler active antenna-based payload architecture and without requiring any user channel estimation. The MB MIQDP-RRM M-MIMO pragmatic solution allows to achieve higher satellite broadband throughput compared to a conventional four colors frequency reuse scheme (CFR) with affordable complexity for both space and ground segments. At the same time we show that by non-conventional CFR multi-beam array design, the performance gap compared to MB MIQDP-RRM M-MIMO can be significantly reduced.

Coherent radar processing in sea clutter environments, part 2: adaptive normalised matched filter versus adaptive matched filter performance

The maritime surveillance performance of the adaptive normalised matched filter (ANMF) detector structure against real multichannel, medium grazing angle, radar sea clutter data processed via space-time adaptive processing (STAP) is assessed and shown to exhibit constant false alarm rate (CFAR) performance characteristics which diverge from predictions over a large segment of the endo-clutter spectrum. The non-CFAR behaviour is linked to the existence of a two-component clutter model composed of contributions from Bragg and fast scattering mechanisms.

On the performance of a persymmetric adaptive matched filter

We examine the adaptive detection problem in the presence of colored noise with an unknown covariance matrix, by exploiting a persymmetric structure in the received signal. The persymmetric adaptive matched filter (PS-AMF) is used to address this problem, which can significantly alleviate the requirement of secondary data. In G. Pailloux et al. “Persymmetric adaptive radar detectors,” (2011) the probability of false alarm of the PS-AMF has been obtained in terms of the Gaussian hypergeometric function. In this paper, finite-sum expressions for the probability of false alarm of the PS-AMF are derived, which are more convenient to use in calculating the detection threshold. Moreover, the detection probabilities of the PS-AMF for both nonfluctuating and fluctuating target models are derived. In the fluctuating model, the amplitude of the target echoes is described by a generalized Chi distribution that involves the Rayleigh distribution as a special case. These theoretical results are all confirmed using Monte Carlo (MC) simulations.

Effect of channel impulse response on matched filter performance in the 2013 Target and Reverberation Experiment

Active sonar performance is determined by the characteristics of the target, the sonar system and the effect of the environment on the received waveform. The two main influences of the environment are propagation effects and the contamination of the target echo with a background. The ambient noise and reverberation are mitigated by means of signal processing, mostly through beamforming and matched-filtering. The improvement can be quantified by the signal to noise ratios before and after processing. Propagation effects can have a large influence on the gains obtained by the processing. To study the effect of the channel on the matched filter performance, broadband channel impulse responses were modeled and compared to measurements acquired during the Office of Naval Research-funded 2013 Target and Reverberation Experiment (TREX). In shallow water, a large time spread is often observed, reducing the effectiveness of the matched filter. TREX data show, however, a limited time spread. Model predictions indicate that this could be caused by a rough sea-surface, which while increasing propagation loss, at the same time increases matched filter gain.

Clustering hyperspectral imagery for improved adaptive matched filter performance

This paper offers improvements to adaptive matched filter (AMF) performance by addressing correlation and non-homogeneity problems inherent to hyperspectral imagery (HSI). The estimation of the mean vector and covariance matrix of the background should be calculated using “target-free” data. This statement reflects the difficulty that including target data in estimates of the mean vector and covariance matrix of the background could entail. This data could act as statistical outliers and severely contaminate the estimators. This fact serves as the impetus for a 2-stage process: First, attempt to remove the target data from the background by way of the employment of anomaly detectors. Next, with remaining data being relatively “target-free” the way is cleared for signature matching. Relative to the first stage, we were able to test seven different anomaly detectors, some of which are designed specifically to deal with the spatial correlation of HSI data and/or the presence of anomalous pixels in local or global mean and covariance estimators. Relative to the second stage, we investigated the use of cluster analytic methods to boost AMF performance. The research shows that accounting for spatial correlation effects in the detector yields nearly “target-free” data for use in an AMF that is greatly benefitted through the use of cluster analysis methods.

Development of an efficient hybrid model for range sidelobe suppression in pulse compression radar

An efficient hybrid model that substantially reduces the sidelobe of the compressed output of the binary phase coded waveforms is suggested by suitably combining a matched filter (MF) and a radial function (RF). The sidelobe suppression is achieved by modulating the MF output by the RF output. Simulation study is carried out to evaluate the performance of standard MF, multilayer artificial neural network (MLANN) and radial basis function neural network (RBFNN) based pulse compressors for binary phase coded pulse compression. The evaluation is based on comparative analysis of the peak to sidelobe ratio (PSR) of the compressed output under noisy as well as Doppler shift conditions. The experimental results demonstrate that the performance of proposed method is significantly superior compared to that of the other standard methods. Further, the hardware requirement of the proposed model is significantly less and unlike other neural networks it does not require training operation.

Spectrum Sensing in the Presence of RF Impairments in Cognitive Radio

The spectrum detection performance of a matched filter (MF) in the presence of carrier frequency offset (CFO), phase noise (PN) and time offset is analyzed. Detection performance is derived theoretically and confirmed through simulation in the presence of CFO. The results have shown that in the presence of CFO, the MF may be outperformed by an energy detector (ED) over a range of CFO values. The authors propose three spectrum sensing techniques that are robust to CFO. The first technique is called the block-coherent detector (BLCD) with a suboptimal number of blocks (N/2). The second technique is called second-order matched filter-I (SOMF-I), the detection performance of which has been studied both theoretically and confirmed through simulation. The last technique is named the second-order matched filter-II (SOMF-II) and is a modified version of (SOMF-I) but with a superior performance. The presence of PN and time offset and their effect on the detection performance is then examined via simulation. The results have shown that PN has not affected the detection performance of SOMF-I and has only a slight effect on the MF performance. But the time offset has seriously degrades the MF, ED and SOMF-I. Also, the results have shown that the performance of SOMF-I is much less affected by noise uncertainty compared to the ED. Finally, the throughput of SOMF-I has been studied in the presence of CFO and the results have shown that its performance is only marginally degraded compared to the MF.

Task-based comparison of inverse methods in magnetoencephalography

Magnetoencephalography (MEG) provides unique insights into the spatio-temporal dynamics of neural activation in the human brain. Unfortunately, the accuracy with which neural sources can be localized is limited by the highly illposed nature of the inverse problem. A large number of inverse methods have been proposed that deal with this illposedness using a range of different modeling and regularization procedures. Here we describe an objective task-based framework for comparing different inverse methods. Using the free-response receiver operating characteristic (FROC) we compare the performance of matched filters, cortically constrained dipole scanning, and minimum norm imaging methods for the task of detecting focal cortical activation. Our results indicate that the scanning methods outperform matched filters and minimum norm imaging for the case of one and two 2 cm2 patches of cortical activity when the dynamics of the two patches are both strongly and weakly correlated and irrespective of the spacing of the two activated regions.

Realistic matched filter performance prediction for hyperspectral target detection

The linear matched filter is widely used in hyperspectral target detection applications. Its detection performance is typically evaluated based on Gaussian (or normal) probability density models. However, it is well known that hyperspectral backgrounds exhibit "long-tail" behavior, which cannot be accurately modeled by normal distributions. In this work, we model the distribution of hyperspectral backgrounds using a more accurate model based on elliptically contoured multivariate t distributions. For the target class, we still use a normal distribution model, because the body of the distribution is important for detection applications. We show that the tails of the background distribution deteriorate the performance of the matched filter. In this sense, performance predictions based exclusively on normal models, which ignore the long tails, can be inaccurate and overly optimistic. The proposed technique, which allows for an easy control of the tail's heaviness, can be a very useful tool for evaluating target detection performance for backgrounds with both light and heavy tails.

Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery

The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified k-means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. The authors show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional k-means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new "extreme" centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images.

Validation of windblown radar ground clutter spectral shape

We investigate the robustness of the linear matched filter (MF) operating in a Gaussian environment in the presence of a mismatch between the design clutter-power spectral density (PSD) shape and the actual one. The Gaussian, the power-law (PL), and the double-exponential spectral models have been considered with the goal of investigating which one fits best for windblown foliage. We analyze the MF performance in terms of improvement factor, probability of false alarm, and probability of detection by making use of the theoretical models and measured X-band ground clutter data. The numerical results validate the double-exponential spectral model for windblown foliage by showing that the differences in performance prediction between using measured clutter data and modeled clutter data of various spectral shapes (viz., Gaussian, FL, and double-exponential) are minimized when the spectral model employed is of double-exponential shape.

Bounds on matched filter performance in doubly dispersive Gaussian WSSUS channels

The matched filter bound (MFB) is derived for doubly dispersive Gaussian WSSUS channels. By means of the Karhunen-Loeve expansion both monotonically increasing and monotonically decreasing sequences of error probabilities converging towards the MFB are presented in closed form as a function of the pulse shapes and the scattering function.

Chip-delay locked matched filter for DS-CDMA systems using long sequence spreading

This paper considers an improved single-user detection technique for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems using long sequence spreading (random-CDMA) Most of the known detection schemes for DS-CDMA suffer from either poor performance under power-imbalance (near-far like) conditions, excessive complexity, or incompatibility with systems employing long sequence spreading. To address these problems, this paper considers a signal-to-noise ratio maximizing linear time-invariant filter for one-shot bit symbol detection exploiting some information about the interferers. This filter, referred to as the chip-delay locked matched filter (CLMF), exploits the cyclostationarity in multiple-access interference, and it can offer good near-far resistance while remaining suitable for systems with long sequence spreading. The CLMF requires knowledge of interferers chip delays and signal powers; however, knowledge of their pseudonoise sequences is unnecessary. This paper also demonstrates the improvement in performances offered by the CLMF over other single-user receivers such as the conventional matched filter and noise-whitening matched filter performance is evaluated in terms of probability of outage for single-rate and dual-rate DS-CDMA systems using bandwidth-efficient chip pulses, over a single-path additive white Gaussian noise channel. Errors in the interferer chip delay estimates degrade the CLMF performance. However, if the root-mean-square value of these errors is less than 5% of the chip interval, then this degradation is small.

Estimation of coherent detection performance for spread scattering in reverberation-noise mixtures

Narrow-band matched filter processing gain is estimated for medium- and high-frequency active sonars for which the random backscattering processes are assumed to be wide sense stationary in time frequency and uncorrelated in delay-Doppler wide sense stationary and uncorrelated scattering [WSSUS conditions]. Echo and reverberation processes that are WSSUS are described by two-dimensional scattering functions defined in the delay-Doppler plane. The average receiver responses are estimated from the convolution of the appropriate scattering function with the waveform ambiguity function. Estimates of matched filter processing gain are derived for continuous wave (CW) linear frequency modulation (LFM), and discrete frequency shift keyed (FSK) (hop code) waveforms reflected from point and uniform delay spread scatterers masked by reverberation. These bound matched filter performance for a particular waveform and interference distribution since most delay spread scattering falls somewhere between these extremes. The scattering and ambiguity functions are modeled by bounded constant amplitude functions in delay-Doppler that permit the convolutions to be approximated by overlapping area calculations. The results are presented in tabular form as simple formulas that are functions of the reverberation, noise, and waveform parameters. The estimates are shown to be consistent with processing gain measurements made from multiple realizations of synthesized and in-water data.

Distribution of the global maximum of a Gaussian random field and performance of matched filter detectors

A simple model is developed that successfully captures the point statistics of the global maximum of a Gaussian random field of reasonably large support. Tractable expressions for the probability that the global maximum of the field exceeds an arbitrary threshold are provided; this probability is shown to be completely insensitive to the fine details of the correlation function of the random field. The validity of these results is assessed by comparison with computer simulation, where excellent agreement is obtained. These results are potentially most valuable for the assessment of the performance of matched filters for large-scale feature detection in images.

Robust detection of small stochastic signals

We consider the problem of detecting a stochastic signal in white not-necessarily-Gaussian noise, using vector valued observations. The locally optimal detector is presented and its performance evaluated. The least-favorable signal spectrum and noise density (over specified classes) are found, and it is shown that the detector using these least-favorable assumptions is minimax robust. The class of spectra is that of any stochastic signal of specified power whose spectrum can be bounded from above and from below by two given positive functions. The class of densities is the /spl epsi/-contamination model. We present examples of the performance achievable with the robust detector in one of these the spectral uncertainty class corresponds to the unknown Doppler shift of a radar return signal. It is demonstrated that the standard matched-filter's performance degradation with increasing Doppler shift can be avoided almost entirely through use of the robust processor.