Novel Radar Techniques and Applications

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Preface to Volumes 1 and 2

Editor Biographies

Richard Klemm was a member of NATO's AGARD AVP and RTO-SET panels and chaired various AGARD and RTO symposia. He is a member of the IEEE AES Radar System Panel and the editorial board of the IET Proceedings on Radar Sonar and Navigation.

List of Authors

Linderman, Richard Office of the Secretary of Defense USA Lombardo, Pierfrancesco DIET Università di Roma 'La Sapienza' Italia Maio, Antonio de Universita` degli studi di Napoli Fed II Italia Maslikowski, Lukas Varsavia University of Technology Polonia Myakinkov, Alexander Nishny Nowgorod Technical State Università della Russia.

List of reviewers

Real aperture array radar

Introduction to real aperture array radar Ulrich Nickel 1

Estimating the dynamic target parameters is the subject of target tracking, which is discussed in Part II of Part 2 of this book. The purpose of the first part of this book is to introduce these problems for the simpler case of a true diaphragm.

Target parameter estimation and array features

Introduction

This provides a variety of methods to control the amount of data and the complexity of the signal processing and tracking algorithms. The antenna array must be designed to meet all the requirements of the radar system.

Basic concepts and results of array antennas

This has the advantage that it requires little knowledge about the interference scenario, but requires very precise knowledge about the array transfer function ('the array manifold'). This is the basis of the well-known monopulse formula for the approximate angle ^u¼u0mRefD=Sg.

Design factors for arrays

Sub-array design procedure for low sidelobe sum and difference beams
Beam scanning at sub-array level and sub-array modification

The positions of the super-array elements are now calculated by the property that the sub-array patterns Fl(u) must be equal to flðu;vÞexp 2pfð 0ðrlðuu0Þþ rlðvv0ÞÞ=cÞ(superarray notation) for suitable superarray element patternsfl( u). This freedom also allows to normalize the power of the sub-array outputs as such.

Figure 1.1 Array factor for 16 element ULA at l/2 spacing and 0.6l spacing and look direction 60 (u 0 ¼ 0.875)

Array accuracy requirements

Eigenvalue leakage is also particularly strong for small numbers of samples for estimating the covariance matrix. In any case, whether produced in the analog or digital domain, any uncompensated limiting effects will give rise to dispersive effects leading to additional eigenvalues in the covariance matrix similar to the effects described in Sections 1.4.1 and 1.4.2.

Figure 1.11 shows the eigenvalues of a covariance matrix for narrowband beamforming (phase shifting only) with and without errors and also the case of zero bandwidth for reference

Antenna pattern shaping

Optimal deterministic pattern design. The rationale for low sidelobes is that we want to minimize some of the unknown interference power coming through the sidelobes. To choose the function p, we consider that for the global minimization of the side slits when W=fu2R2u2þv21g, the main beam should be excluded from the minimization by setting p=0 on this set of directions (actually a slightly larger area is recommended, e.g. the width from null-to -null) to allow a specified extension of the main beam.

Figure 1.13 Low sidelobes by Gauss, Taylor and Chebyshev amplitude tapering (from [4])

Adaptive interference suppression

Rank A is also called the required number of degrees of freedom (dof) of the weights w. The number and size of the subarrays thus determine the effectiveness against main beam jammers. Thus, for diagonal loading, the sample size can be much smaller than the matrix dimension.

Signal Inclusion: Inclusion of a desired signal in the exercise data results in signal suppression.

Figure 1.14 Principle of sidelobe canceller (SLC)

Parameter estimation and super-resolution

A weighting of the subspace components is counterproductive to the desired high peaks of the density. This is the basis of the alternating projection (AP) method [52], which corresponds to the IMP (Incremental Multi-Parameter) method, [11 p. This leads to an iteration with an update vector consisting of the gradient divided by the decoupled sum beam, i.e.

One of the main problems is the numerical effort to find the maximum M (one-dimensional optimization or 1-dimensional optimizations M for a linear antenna).

Figure 1.22 MUSIC spectra of two targets at 0.6BW separation with a planar array of 8 elements (7 elements irregularly distributed on a ring and with one in the centre)

Extension to space-time arrays

The selection of initial values is motivated by the requirement that the directions should be widely separated within the acceptable search region. Test design must be compatible with this threshold region to yield consistent results of joint detection and evaluation resolution. The challenge is to find a solution that meets the common limitations of the given aerial platform antenna hardware, the field of view resulting in a particular type of clutter, and the fast and robust estimation algorithm.

The effect of these configurations on the eigenvalue spectrum, particularly the amount of leakage eigenvalues, and the performance of various subspace methods has been discussed in detail in [2, Chapter 16].

Embedding of array processing into full radar data processing

The errors increase for targets on the skirt of the main beam and close to the jammer. For odd irregular sub-arrays as for the generic array (Figure 1.7), the different contributions of the sub-arrays can be compensated. For comparison, the performance of the corresponding quantities with the GLR protection channel is depicted in Figure 1.33.

In case the suggested viewing direction is in the jammer notch, we select a custom direction on the skirt of the jammer notch.

Figure 1.27 Bias and standard deviation ellipses of generalized monopulse with adapted sum and difference beams for different target positions, (from [4])

Conclusions and final remarks

If we use only ABF and adaptive monopulse, there will almost certainly be track loss near the jammer. The custom beam steering rules developed for adaptive tracking are radar management measures. Nickel 'Determination of the dimension of the signal subspace for a small sample size'.IASTED Int.

Pisarenko 'On the estimation of spectra by means of nonlinear functions of the covariance matrix'. Geophys.

Robust direct data domain processing for MTI

Introduction

To overcome this problem, [12] proposed a D3-STAP filter binding with several constraints covering the uncertainty range of the target parameters. In particular, a robust implementation of the D3-STAP filter (e.g., RD3-STAP) is described to avoid the aforementioned target self-cancellation effect [13]. Following this approach, the design of the RD3-STAP filter is reformulated in the context of convex optimization [15].

Some applications of the RD3-STAP filter for synthetic aperture radar (SAR) and for target DOA estimation are presented in Section 2.5.

Notation and signal model

As can be seen, the linear system in (2.9) tends to zero only the interference matrix F2, while maintaining a non-zero gaze direction constraint in the nominal target direction (first row of F). It should also be noted that the D3-STAP filter derived from solution (2.9) corresponds to the so-called forward (FW) implementation [11 Chapter 12], where the term FW comes from the observation that the vector xis spanned from the first to the last element in the weighted subtractions in (2.6). This can be easily achieved by replacing one or more rows in matrixFin (2.8) with .

In the next section, a robustness of D3-STAP (i.e., RD3-STAP) is presented to solve this problem.

Robust D 3 -STAP

Second, RD3-STAP reformulates the D3-STAP filter design in terms of convex optimization, [15], which can be easily solved with ready-to-use toolboxes [18,19]. DRTs with overlapping sub-arrays (such as those shown in Figure 2.1b) can also be applied in the RD3-STEP case, as first introduced in [20]. From (2.24) the RD3-STEP filter to DRT can be derived as shown in the preceding paragraphs.

In the next paragraph (see also [20]), a simulative example is shown that demonstrates how DRTs can be effectively applied to double canceller RD3-STEP filtering.

Figure 2.1 Examples of dimension reducing transforms. 2006 IET. After [1]

Results of RD 3 -STAP

In the second subplot of Figure 2.9, the Doppler cutoff of the hard gate of the target range is reported after applying the adjacent bin STAP post-Doppler filter. In fact, the presence of the weak target in the secondary data has only a limited effect on the detection of the strong target. On the other hand, the weak target is completely canceled out by the presence of the strong target in the corresponding secondary data.

The corresponding Doppler master slice of the input signal spectrum is shown in the top graph in Figure 2.11.

Applications of RD 3 -STAP

Signal model
Integration with the focusing step
Case study analysis
High signal bandwidth
Comparison with MLE

In Figure 2.15 the output of the RD3-STAP filter is reported as a function of the Doppler frequency, that is yðfDÞ. Finally, note also the smooth behavior of the RD3-STEP filter transfer function in the vicinity of the target position in Figure 2.14. Following the previous considerations, imaging the moving targets after applying the RD3-STAP filters in the Doppler domain simply requires an IFFT.

By simple inversion of (2.40), the closed expression of the RD3-ABF based DOA estimation can be recovered.

Figure 2.12 Block diagram of the integrated technique. 2012 IEEE. After [26]

Conclusions

Glossary

SINR signal-to-interference-plus-noise ratio STEP space-time adaptive processing ULA uniform linear array. 25] Ender, J.H.G.: 'Space-time processing for multichannel synthetic aperture radar', Electronics Communication Engineering Journal, 1999, 11, February (1), pp. 26] Cristallini, D.: 'Harnessing Robust Direct Data Domain STAP for GMTI in Very High Resolution SAR',IEEE Radar Conference (RADAR), May 2012, pp.

27] Cristallini, D.: 'Target DOA'stimation based on robust deterministic STAP', Proceedings of 9th European Conference on Synthetic Aperture Radar, EUSAR 2012, Prill 2012, pp.

Array radar resource management Alexander Charlish* and Fotios Katsilieris*

Management architecture

Priority assignment: The priority assignment module assigns a priority value to each radar task, which represents the task's right to antenna use relative to other tasks. Task Management: The task manager is responsible for selecting control parameters for each radar task based on its priority and other task-specific requirements. The task manager issues work requests to the scheduler based on the selected control parameters for the task and its priority.

Scheduler: The scheduler is responsible for creating a timeline of tasks from the multiple work requests.

Figure 3.1 Core components for an electronically steered phased array antenna.

Task management

Search lattice and beam spacing
Revisit interval time and dwell duration
Pulse repetition frequency selection
Non-uniform search parameters
Active tracking
Dwell length adaptation
Waveform selection and adaptation

The waveform used in the confirm hold may match the alert generated by the original search hold. For example, the confirmed transmit energy and thus the dwell length can be varied based on the measured SNR in the original alert. For actively tracked targets, adaptive tracking approaches can be used that aim to coordinate the time of the revisit interval and thus the time of the next measurement based on target maneuvers.

An arbitrary revisit interval can be selected for active tracking, or the revisit interval can vary based on the target threat or advantage.

Figure 3.2 Antenna (u v) coordinate system (a) u v coordinates relative to antenna plane

Priority assignment

Fuzzy values can be assigned to variables that represent characteristics of the guard sector or target track. Fuzzy if-then rules can then be applied to determine the priority of the target track or guard sector. However, this approach only makes sense if the priority value affects the radar behavior.

Fuzzy logic approaches can also be applied directly in the resource management process instead of simply determining the priority assignment.

Table 3.2 Decision tree for search sector priority assignment using fuzzy logic, taken from [68]

Scheduling

Because frame-based schedulers optimize the placement of the task in the assignment frame, they can generate good quality schedules that minimize the delay of the tasks and account for the cost of postponing the tasks. The occupancy depends on the fluidity of the tasks, that is, the time between the desired time and the earliest and latest time (respectively referred to as the endl in Figure 3-11) at which the task can be scheduled. A degenerate version of the frame scheduler described in [71], where all tasks were treated as primary tasks.

It can be seen that as work flow increases, more jobs can be scheduled and the occupancy increases regardless of the use of priorities.

Figure 3.12 Achieved occupancy of each scheduler as function of job fluidity

Summary

Hughes, “Medium PRF set selection using evolutionary algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. Matthew, “Mean-PRF Radar PRF Selection Using Evolutionary Algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. Blackman, "On Phased Array Radar Tracking and Parameter Control," IEEE Transactions on Aerospace and Electronic Systems, vol.

Bar-Shalom, ‘Benchmark voor radartoewijzing en tracking in ECM’, IEEE Transactions on Aerospace and Electronic Systems, vol.

Imaging radar