Novel Radar Techniques and Applications, Volume 2.pdf

Wolfgang Koch is a member of the IEEE, where he serves on the IEEE Transactions on Aerospace and Electronic Systems (T-AES) and as a member of the Board of Governors of the Aerospace and Electronics Systems Society (AESS). He is an IEEE Distinguished Lecturer and founding president of the German IEEE AESS Chapter.

Waveform diversity and cognitive radar

This opens up enormous possibilities and is also one of the cornerstones of cognitive radar. In many cases, the transmitting source is not under the control of the passive radar.

Figure 1 The perception–action cycle of cognitive radar 2006 IEEE.

Radar emission spectrum engineering Shannon D. Blunt 1 , John Jakabosky 1

Introduction

Furthermore, it is useful if this representation provides the means to parameterize the waveform for optimization of the resulting physical signal according to attributes such as distance sidelobes and Doppler tolerance. This implementation is a modified form of the continuous phase modulation (CPM) scheme [27] commonly used for aeronautical telemetry [28,29], deep-space communication [30] and forms the basis of the BluetoothTM wireless standard [31] .

Polyphase-coded FM

The choice of filter to shape g(t) also affects the waveform and could also become a free parameter for optimization (although this is not considered here). Regarding the LFM waveform, it is also observed in Table 1.2 and Figure 1.11 that all the optimized waveforms include a small degradation of area resolution.

LINC-optimized waveforms

In [35] a LFM waveform with BT¼64 applied a Tukey taper via this LINC method so that the pulse rise/fall effectively occurs during the first and last quarter of the pulse. The hardware setup is the same as for the results in Figure 1.13 from the previous section, apart from the addition of the 180 connector and a second AWG. According to Figure 1.19 captured with a real-time spectrum analyzer (RSA), the taper provides approximately 15 dB of additional spectral inclusion in the out-of-band region.

However, this proof of concept demonstrates the potential for Figure 1.19 LFM spectrum with (lower trace) and without (higher trace).

Figure 1.17 Notional spectral content of a 64 ms pulse modulated with an LFM waveform of 1 MHz bandwidth (with and without inclusion of the pulse rise/fall)

Spectrally shaped optimization

The trade-off for this small SNR loss can be observed in Figure 1.22 in which the autocorrelation is shown for the intermediate stage as well as the final stage, with and without inclusion of the taper. Finally, Figure 1.24 illustrates the corresponding filter response for a loopback measurement when this jointly optimized emission is implemented on the radar test bed used in the previous sections. In contrast, the RMS average cross-correlation between adjacent segments increases by 1.5 dB (see Figure 1.26) for the optimized measurement relative to the initialization.

A similar improvement is seen in Figure 1.29 for the zero-Doppler integrated cross-correlation response between adjacent segments.

Figure 1.21 Pulse amplitude envelope after optimized joint waveform/taper optimization with BT ¼ 128 ( 2015 IEEE, reprinted with permission from [36])

Conclusions

Savy, 'Challenge problems in spectrum engineering and waveform diversity', IEEE Radar Conf., Ottawa, Canada, 29 Apr. Stiles, 'A rapid method for designing optimal transmit codes for radar,' IEEE Radar Conf., Rome, Italy, May 2008. Cohen, 'Optimization of polyphase coded FM waveforms in a LINC transmission architecture', IEEE Radar Conf., Cincinnati, Oh, pp.

Himed, "Radar-Focused Waveform Design with Disjoint Spectral Support," IEEE Radar Conf., Atlanta, GA, p.

Adaptive OFDM waveform design for spatio-temporal-sparsity exploited STAP radar

Introduction

The estimation accuracy of the interference covariance matrix and thus the efficiency of the STAP filter depends on a number of homogeneous secondary measurements used (given by the Reed-Mallett-Brennan (RMB) rule) [10]. However, in practical scenarios, the calculation of the output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Obtaining good estimates of the interference covariance matrix has a large impact on the achievable improvement in output SINR via adaptive waveform design.

Next, we provide a closed form expression of the optimal OFDM coefficients by reasonably considering that the estimation accuracy of the interference covariance matrix depends insignificantly on the signal parameters.

Sparse-measurement model

We also discuss in detail the effects of interfering signals (interference and thermal noise) on the received signal. Based on the statistical assumptions from the previous section, we know that the optimal detector is compared to a predetermined threshold [1]. A threshold is set to achieve a certain probability of false alarm (PFA) of the decision problem.

Therefore, the estimate of the total interference covariance matrix is given by RbI ¼RbCþhILMN, where h¼10s2 is chosen as 10 times the white noise level, similar to the approximation of [63].

Figure 2.1 A schematic representation of the problem scenario

Optimal waveform design

In practical scenarios, since the knowledge of RI is unknown, we need to estimate it using the measurements of the secondary series gates. To estimate RI efficiently, we first use the sparse retrieval technique to obtain the estimates of xC and then premultiply them with the known sparse measurement matrixY. Now, since we calculate cRI with a sparse recovery procedure, it is important to investigate how different choices of the broadcast signal parameters affect such an estimation procedure.

We characterize the accuracy of the sparse restoration algorithm by calculating the coherence of the sparse measurement matrix as [59–61].

Numerical results

The SINR loss performance of the sparsity-based STAP technique is shown in Figure 2.4. The ROC curves in Figure 2.5 show the detection performance of the sparsity-based STAP approach for two targets zðTIÞandzðTIIÞ, both in the absence and presence of temporal decorrelation effect. ROCs for the sparsity-based STAP method were approx. within 1:2 dB of the optimal performance limit for both target scenarios.

The SINR loss performances of Figure 2.6 show that, both in the absence and presence of.

Figure 2.3 Estimated clutter spectra (at L ¼ 4 subcarriers) of the sparsity-based STAP method in the (a) ideal scenario (no decorrelation) and (b) presence of temporal decorrelation [the colorbar is in linear scale]

Conclusions

Ward, "Adaptive spatio-temporal processing for airborne radars," Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, Tech. Nehorai, “MIMO OFDM radar with mutual information waveform design for low grazing angle tracking,” IEEE Trans. Nie, "Sparsity-based adaptive spatio-temporal processing using complex-valued homotopy technique for airborne radars".

Sen, 'Low rank matrix decomposition and spatio-temporal sparse recovery for STAP radar', IEEE J.

Cognitive waveform design for spectral coexistence

Introduction

Some iterative algorithms are introduced in [39] for the joint design of the transmit signal and the receive filter that achieves frequency stopband suppression and amplitude sidelobes minimization. In [45], a friendly spectral radar waveform design is considered to enable the coexistence of the radar with one or more communication systems. In Section 3.2, the model for the radar transmitting signal, the description of adjacent wireless systems and the formulation of the waveform design problem are reported.

In Section 3.4, the signal-dependent interference environment is discussed and the efficiency of the described procedure is evaluated.

System model and problem formulation

This motivates the study of the accessible I/S region for each fixed similarity codec0, namely the set of acceptable interferences and similarity levels. Therefore, it is possible to relate to the boundary of the feasible region several important radar performance metrics, such as ISL, PSL and, obviously, the produced interference power. For example, with reference to Figure 3.2, considering ðEI;eÞ equal to point A, the radar frequency coexistence with.

The interested reader may refer to appendix 3.6.1 for all analytical details related to the study of the I/S feasible region.

Figure 3.1 A pictorial representation of the REM and its usage in a cognitive radar

Signal-independent interference scenario

Figure 3.4(a) shows the ESD of the synthesized signals versus the normalized frequency along with the reference code. As expected, a proper choice of design parameters enables good interference rejection properties as well as high SINR values. In Figure 3.4(b) a performance analysis in terms of autocorrelation characteristics of the designed waveforms is provided.

However, the smoother behavior of the synthesized signals according to Algorithm 1 complies with the design criterion P.

Figure 3.4 (a) ESD; (b) Squared modulus of the ACF. Green curve: reference code c 0 ; blue curve: Algorithm 1, E I ¼ 0 : 066, e ¼ 0 : 264; magenta curve: Algorithm 1, E I ¼ 0 : 066, e ¼ 0 : 444; black curve: Algorithm 1, E I ¼ 0 : 066, e ¼ 0 : 654; cyan cu

Signal-dependent interference scenario

In addition, it can be observed that by increasing the similarity parameter e, ever smarter distributions of the usable energy are achieved. Let us now evaluate the performance of the waveform design technique described in Appendix 3.6.3, i.e. algorithm 3. Moreover, by increasing the similarity parameter, namely increasing the available degrees of freedom, increasingly smarter distributions of the usable energy are achieved.

In fact, as in Figure 3.4(a), there is a lower and lower transmitted radar power according to common frequencies and improved jamming rejection capabilities.

Figure 3.5 depicts the block diagram of the optimization procedure adopted to handle Problem P when the radar system operates in signal-dependent interference.

Conclusions

The curves show that Algorithm 3 is able to appropriately control the amount of energy produced over the shared frequency bands. Finally, in Table 3.1, the ISL and PSL of the cross-correlation functions (CCFs) of the radar codes and receive filters, corresponding to the operational point ðEI;e3Þ ¼ ð0:017;0:3Þ, for different values of iteration number (n are given. The values in the table reflects the ability of the considered joint transmit-receive optimization procedure to iteratively achieve better and better signal-dependent interference suppression levels.

Further research activity is needed towards the synthesis of new advanced adaptive receivers in PBR with improved interference suppression capabilities, the development of commensal radar strategies with improved control of LTE modulation parameters, and the design of radar embedded communications. able to leverage multiple domains to increase data rates.

Appendix

Proof of Proposition 3.6.1
Proof of Proposition 3.6.3 Notice that problem ð QP Þ e is equivalent to
Feasibility of P 3

Let E0I

Precisely, its optimal solution can be calculated in polynomial time (using the first rank of the matrix decomposition theorem [55, Theorem 2.3]), assuming an optimal solution to the SDP problem.

Noise Radar Technology

Introduction

Stationary target model
Constant radial velocity model
Constant acceleration model

The distance resolution of the noise radar, defined as the width of the correlation peak (4.4), can be expressed as. The term is the integration gain of the treatment (the signal-to-noise ratio after treatment to the signal-to-noise ratio before treatment). It is noteworthy that in the case of constant acceleration the output of the correlation receiver is three-dimensional.

Acceleration of the target can also be estimated after estimating the target range and Doppler shift.

Figure 4.1 The desired shape of radar waveform ambiguity function; (a) ideal without range and Doppler sidelobes (noise floor), (b) practical – with sidelobes in range and Doppler

Clutter and direct signal cancellation

The situation can be much worse when a close target or ground clutter radar cross section is significantly higher than that of the target. In Figure 4.17, the cross section of the zero Doppler beam of the ambiguity function before and after the application of clutter cancellation is presented. Two antennas of the radar, mounted on a roadside trailer, are visible on the right.

In Figure 4.19, the cross-ambiguity function of the received signal after clutter cancellation is presented.

Figure 4.15 Masking effect in noise radar

MIMO noise radars

The noise radar processing is based on the correlation of the received signal with a template. The point is that it is not a feature of the MIMO mode, but of the array geometry itself [62]. Calibration on a stationary corner reflector proved inaccurate due to the influence of the ground echo from the same range cell.

Performance of MIMO Radar Systems: Advantage of Angular Diversity,” in Conference Record of the Thirty-Eightth Asilomar Conference on Signals, Systems and Computers, vol.

Figure 4.20 Radiation-pattern comparison

Cognitive radar management Alexander Charlish 1 and Folker Hoffmann 1

Cognitive radar architecture

For example, at the signal level, Adaptive Space-Time Processing techniques [9] apply two-dimensional filtering based on learned statistics of the interference environment to maximize the signal-to-interference ratio. At the measurement level, learned statistics of the current clutter environment help in robust detection in complex environments [10]. This is not surprising, as the management branch depends on information from the assessment branch to apply radar management.

This chapter focuses on techniques for the radar management branch that enable the manifestation of the cognitive processes of attention and anticipation.

Figure 5.1 Hierarchy for assessment and management

Effective QoS-based resources management

Problem formulation
Optimality conditions
Problem formulation
Optimality conditions
Quality of service resource allocation method Q-RAM
Continuous double auction parameter selection
Active tracking performance models
Search performance models

The problem of resource allocation is linked to the problem of selecting control parameters for each task so that the allocated resources are used optimally. Based on these functions and the utility function given in (5.4), the resources, quality and usefulness of control parameter selections can be evaluated. Based on the resource and quality functions, and the utility function of the resource allocation problem (equation (5.4)), a constrained optimization problem.

The control parameter selection problem can be related to the resource allocation problem in (5.7) by introducing a change of variable.