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This paper reports on the design and initial evaluation of a low-cost multistatic radar system that exploits digital components. The system is based on a commercial-off-the-shelf and open architecture approach, using a direct digital synthesiser, a field programmable gate array and a digital signal processor as core components. Instrument function testing, calibration and the results of the initial field tests are reported. Some of the first multistatic experimental results are reported and demonstrate a number of aspects of the performance of such a configuration. The advantages and limitations of a low-cost digital radar design are discussed and further system development possibilities outlined. This system will enable the collection of a wide range of novel multistatic data and has the potential to demonstrate a number of new radar applications.

High-frequency (HF) radars require large-array antennas to achieve narrow beamwidths and are required to discriminate targets with low Doppler shifts compared with microwave radar. Superresolution processing can discriminate signals at separations much less than the beamwidth or conventional Doppler bin width (or conversely, achieve a given minimum resolvable separation with a smaller array or less time-on-target). Simulation results and analysis of real radar data presented here show at least a 2-fold improvement compared with conventional processing, assuming simple theoretical gain manifolds. A novel polarisation-sensitive superresolution algorithm applicable to skywave HF radar is also discussed.

The problem of radar target classification is examined for the case when more than one perspective or viewing angle of the target is available to the sensor. Using full-scale target signature measurements as the source data, it is shown how, for the first time, multiple perspectives enhance the classification performance. Indeed this is the case even if only one additional perspective is available for exploitation. Further, we explore the classification performance both as a function of the number of perspectives and of the signal to noise ratio. Three approaches to high range resolution profile multi-perspective classification have been implemented. This removes any possible bias that could be introduced by a single individual classifier. The results show, for all three, a consistent improvement in the classification performance, as the number of perspectives is increased. The techniques employed also provide considerable insight into the classification process highlighting the degree of complexity of this extremely challenging problem.

The management of radar resources in a multifunction radar is central to unlocking the potential offered by electronic scanning. Scheduling is an important sub-problem of radar resource management because of the strong correlation between how tasks should be performed, as well as the time available to carry them out and their timeliness. Two scheduling algorithms previously reported in the literature are compared in order to investigate differences in their performance. This provides new insights which may be invaluable in the development of future optimal schedulers. A detailed model of a multifunction radar has been developed to enable comparison using the same operational conditions. The radar is assumed to operate in both ‘underload’ and ‘overload’ situations. When not fully loaded (i.e. underload), the two scheduling algorithms perform in significantly different ways. However, in the more important case of overload situations, where there are insufficient radar resources to carry out all necessary tasks, both algorithms provide similar performance. Nevertheless, there still remain differences that require careful understanding when selecting the most appropriate scheduler.

The unwanted radar echoes from the ionosphere are collectively called ionospheric clutter. It has proved to be the greatest impediment to achieve consistently good performance in long-range detection of surface vessels and sea-state monitoring for high-frequency surface wave radar (HFSWR). Field experimental data recorded by the HFSWR OSMAR2003 (Ocean State Monitor and Analysis Radar, manufactured in 2003) has been used in detailed investigations of the directional characteristics for this ionosphere clutter, leading to the development of effective mitigation techniques based on antenna design and adaptive signal processing. Particular attention is given to the amplitude and phase relationship among multiple spatial channels for two types of ionospheric clutter. Preliminary experimental results show that the random gain and phase variation of the antenna pattern overhead null destroyed the amplitude and phase consistency among channels. As a result, no significant measured directivity is observed in this type of specular clutter. For the incidence clutter from a lower elevation angle, it is observed that the spread clutter possesses high directivity. A nonlinear receiving array composed of multiple V-shaped antennas without deep and broad null at near-vertical incidence is proposed for adaptive ionospheric clutter suppression.

A phase-modulated surface (PMS) has been shown to provide an effective method of dynamically controlling the scattering from simple planar or cylindrical targets. Real radar targets however have complex geometry comprised of many scattering centres, and multiple interactions may occur between these. Therefore, the authors explore whether the PMS technique is still effective when applied to more realistic target geometries.

An accurate approach to power budget analysis for forward scattering radar with application to the detection of ground targets is presented. A modification of the range equation is used, and the results of experimental testing for its confirmation are given.

The performance of netted radar is evaluated in terms of a netted version of the radar equation and the ambiguity function. A software simulation has been developed to allow assessment of netted radar sensitivity and ambiguity properties in both two-and three-dimensional space. Simulation results demonstrate that coherent netted radar sensitivity depends not only on the radar parameters, but also on the numbers, locations and specification of transmit and receive nodes. The netted radar ambiguity function is affected by a combination of many factors. In addition to those that determine the form of the monostatic ambiguity function, the locations of transmit and receive nodes as well as the position and velocity of the target all are shown to play an important role in evaluating system performance. As a result of these dependencies, it is shown that there is severe degradation of range and Doppler resolution when a target crosses sufficiently close to a transmit–receive baseline.

Radar high-resolution range profile (HRRP) has received intensive attention from the radar automatic target recognition (RATR) community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, only the amplitude information in complex HRRPs is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in complex HRRPs may also contain valuable target discriminant information. RATR using complex HRRPs is discussed. The complex HRRPs' feature subspace within each target-aspect sector is extracted via principal component analysis as the corresponding template during the training phase; while in the test phase we decide that a test sample belongs to the feature subspace which has the test sample's minimum reconstruction error approximation. It is shown that the whole process is independent of the initial phases, but exploits the remaining phase information in complex HRRPs. Furthermore, to make the proposed recognition method more practical, a fast time-shift compensation algorithm is proposed. In the recognition experiments based on measured data, the proposed recognition method using complex HRRPs achieves better recognition results than that using only the amplitude vectors of the complex HRRPs.

A novel technique to obtain position, velocity and radar biases estimates of a ship is described by matching ship-borne radar images to geo-referenced satellite images. The matching is performed through the minimisation of the averaged partial Hausdorff distances between data points in each image. The minimisation rapidly yields robust geographical latitude and longitude position measurements, as well as ship heading and radar biases. The accuracy of the measurements is improved by feeding them into a Kalman filter that also allows estimates of the ship's velocity to be obtained. The method provides an alternative effective position sensor for GPS denied environments, which may also be employed for automatic radar calibration of bearing and range biases or for indoor autonomous mobile robot navigation.

The information content of radar target signatures is a key aspect for automatic target recognition. The role of high-range resolution is investigated as a function of the illuminating wavelength. The classification performance is evaluated using (i) full-scale 2D inverse synthetic aperture radar images obtained from a stepped-frequency chirp modulation radar system and (ii) the corresponding sub-spectra of the target reflectivity function forming lower resolution images at differing centre frequencies. The classification performance as given by different combinations of RF frequencies are also evaluated and compared with the coherent reconstruction from the full bandwidth. Finally, the classification results are also computed using multiple aspects to sense the target. In this way, classification performance as function of diversity space is examined.

In this paper proposed a method that translates the high SNR of a target at a closer distance to a better range resolution without sacrificing the maximum detection range. It is a well-known fact that radar range resolution is a constant parameter unrelated to the detection range. This is because range resolution is dependent on the transmitted and received signal. At the same time, the signal-to-noise ratio (SNR) is dependent on the detection range. For example, at a distance of half the maximum detection range, the SNR increases by 12 dB from that of the maximum detection range and at one-fourth the distance, it increases by 24 dB.

The design of knowledge-based adaptive algorithms has been dealt with for the cancellation of heterogeneous clutter. To this end, the application of the recursive least squares (RLS) technique has been revisited for the rejection of unwanted clutter, and modified RLS filtering procedures have been devised accounting for the spatial variation of the clutter power as well as of the disturbance covariance persymmetry property. Then the authors introduce the concept of knowledge-based RLS and explain how the a priori knowledge about the radar operating environment can be adopted for improving the system performance. Finally, the authors assess the benefits resulting from the use of knowledge-based processing both on simulated and on measured clutter data collected by the McMaster IPIX radar in November 1993.

This paper highlights the extraction of micro-Doppler (m-D) features from radar signal returns of helicopter and human targets using the wavelet transform method incorporated with time-frequency analysis. In order for the extraction of m-D features to be realised, the time domain radar signal is decomposed into a set of components that are represented at different wavelet scales. The components are then reconstructed by applying the inverse wavelet transform. After the separation of m-D features from the target's original radar return, time-frequency analysis is then used to estimate the target's motion parameters. The autocorrelation of the time sequence data is also used to measure motion parameters such as the vibration/rotation rate. The findings show that the results have higher precision after the m-D extraction rather than before it, since only the vibrational/rotational components are employed. This proposed method of m-D extraction has been successfully applied to helicopter and human data.

High-resolution radar sea clutter exhibits long-term temporal and spatial correlation features consistent with the sea surface wave structure. Data are presented showing that there is a transient coherence in the long-term fluctuations of the clutter, which locally appears to show the single periodic fluctuation of a dominant wave, but when averaged over a large area is seen to contain a wide band of fluctuation frequencies. This apparently non-stationary behaviour makes it difficult to predict the local sea clutter level for constant false alarm rate (CFAR) thresholding using conventional techniques. A new method of scan-to-scan CFAR processing is therefore proposed, which takes account of this behaviour. The scheme is analysed and its performance characterised.

A novel approach to the design of spatial arrays is presented. The hybrid array beamformer consists of a sparse array in which some of the elements are arranged periodically, thus creating a periodic sub-array. The outputs of the sparse and periodic arrays are then fused to create a beampattern with good resolution and low peak sidelobe levels. This approach is seen as matching the performance of standard periodic arrays but with a significant saving in terms of the required number of elements. Applications include sonar, radar and ultrasonic systems

Target rotation is the desired motion to obtain descriptive inverse synthetic aperture radar images. However, it can generate blurring effects: the migration through resolution cells. This blurring is more severe when the rotation rate is non-uniform. A method to estimate the non-uniform rotation rate (second-order model) for non-cooperative targets is addressed here. The technique considers the possibility of existence of slant range migration induced by angular motion, unlike other approaches. It needs a previous estimation of the range and radial velocity of two prominent points at two instants and minimises a function in order to estimate the motion parameters, which are used to polar format the radar data. The method is compared with the moving target imaging algorithm of Werness et al. and a time – frequency based technique. Simulated data are used to verify the approach and make the comparisons.

A number of experiments have been carried out in two large wave tanks with three different radar systems. The radar frequency, grazing angle, azimuth angle, water wavelength, wave steepness and the breaking wave strength were all varied systematically. The velocity of the peak Doppler power spectral density was found to depend on the phase velocity of the breaking wave in the radar line of sight, but was independent of the radar frequency. The spectral width depended on the phase velocity of the wave, but not on the grazing angle used. The peak Doppler power and radar cross-section of the breaking waves was found to scale with the radar wavelength (proplambda r 1.5 )

A de-emphasis weighting approach is used to suppress the effect of outliers in background samples during the formation of a sample covariance matrix. The approach is relevant to a broad range of adaptive filtering techniques. Results from processing simulated and real coherent radar data using de-emphasis weighting are compared with results using no outlier suppression and censored sample matrix inversion pruning methods. De-emphasis techniques are shown to produce the most robust detection performance when outliers are present and are also shown to have minimal performance impact when clutter is homogeneous, that is no outliers present.

When the airborne fire control radar operates with the high pulse-repetition frequency (PRF) in the frequency-modulated ranging mode, the range measurement is ambiguous. Here, the problem of target range estimation and ambiguity resolving is converted to the problem of static multiple models estimation and decision. Frequency and pseudo-range measurements are used to update each model of the target with the probabilistic data association and tracking filter. The method to select the PRF used to detect the target is also suggested according to the state estimates of the survived models. Simulation results demonstrate the improved accuracy and convergence when compared with the conventional methods.

Maritime security is related to national economic and political interests and is strategically important. One efficient way to accomplish maritime border protection is to use the netted forward scatter radar (FSR). FSR is a special type of bistatic radar that operates in a relatively narrow scattering area along the transmitter–receiver baseline, where the effect of the electromagnetic waves forward scattering on targets is dominant above other scattering mechanisms, and in this case, a forward scatter (FS) cross section may increase by orders of magnitude in comparison with the monostatic radar cross section (RCS). Considered in this study are the major problems of marine forward scattering radar detection and estimation of length of low-profile (small and slow) marine targets using a pre-processing approach. It is based on the assumption that the variation of the phase and amplitude in the Doppler signal signature is stronger inside the FS zone than in an outside region. Two variants of pre-processing algorithms are presented in the study, one for the envelope and the other for the phase. Both variants are based on the use the local variance filtering. The results obtained prove the sufficient improvement in a signal-to-clutter ratio (SCR). Estimation of the marine target length under the low SCR is designed using the assumption of known or previously estimated velocity. Presented results demonstrate high accuracy of length estimation. Considered steps of targets detection and target attributes evaluations are necessary for maritime targets classification. The designed algorithms are verified using a set of experimental records of signals from different marine targets obtained using marine FSR developed by the teams from University of Birmingham, UK and Sofia University, Bulgaria.

In this study, the authors investigate the application of the Doppler beam sharpening (DBS) technique for angular refinement to the emerging area of low-terahertz (THz) radar sensing. Ultimately this is to improve radar image quality in the azimuth plane to complement the excellent range resolution and thus improve object classification in low-THz radar imaging systems for autonomous platforms. The study explains the fundamental theory behind the process of DBS and describes the applicability of DBS to automotive sensing, indicating the potential for synthetic beamwidths of a fraction of a degree. Low-THz DBS was experimentally tested under controlled laboratory conditions, not only to accurately localised target objects in Cartesian space but also to provide unique object imaging at low-THz frequencies with wide azimuthal beamwidth antennas. It was shown that a stationary (i.e. non-scanned) wide beam antenna mounted on a moving platform can deliver imagery at least comparable to that produced by physical beamforming, be that steering arrays or narrow beam scanning antennas as in the experimental case presented.