MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed transmitters and receivers. While, in a general sense, MIMO radar can be viewed as a type of multistatic radar, the separate nomenclature suggests unique features that set MIMO radar apart from the multistatic radar literature and that have a close relation to MIMO communications. This article reviews some recent work on MIMO radar with widely separated antennas. Widely separated transmit/receive antennas capture the spatial diversity of the target's radar cross section (RCS). Unique features of MIMO radar are explained and illustrated by examples. It is shown that with noncoherent processing, a target's RCS spatial variations can be exploited to obtain a diversity gain for target detection and for estimation of various parameters, such as angle of arrival and Doppler. For target location, it is shown that coherent processing can provide a resolution far exceeding that supported by the radar's waveform.

MIMO Radar with Widely Separated Antennas

The Analysis of Variance.

The measurement selection for updating the state estimate of a target's track, known as data association, is essential for good performance in the presence of spurious measurements or clutter. A classification of tracking and data association approaches has been presented, as a pure MMSE approach, which amounts to a soft decision, and single best-hypothesis approach, which amounts to a hard decision. It has been shown that the optimal state estimator in the presence of data association uncertainty consists of the computation of the conditional pdf of the state x(k) given all information available at time k, namely, the prior information about the initial state, the intervening known inputs, and the sets of measurements through time k. It has also been pointed out that if the exact conditional pdf, which is a mixture, is available, then its recursion requires only the probabilities of the most recent association events. The conditions under which this result holds, namely whiteness of the noise, detection, and clutter processes, were presented. The PDAF and JPDAF algorithms, which carry out data association and state estimation in clutter, have been described. A simple example was given to illustrate how the clutter and occasional missed detections can lead to track loss for a standard tracking filter, and how PDAF can keep the target in track under such circumstances. By using the Monte Carlo in a simulated based surveillance as an exampled shown. The numerous applications of the PDAF/JPDAF illustrated in "Real-World Applications of PDAF and JPDAF" show the potential pitfalls of using sophisticated algorithms for tracking in difficult environments as well as how to overcome them. The effect of finite sensor resolution can be a more severe problem than the data association and deserves special attention.

The probabilistic data association filter

This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with the network, plans, decides and acts on this information. Cognitive networks are different from other "intelligent" communication technologies because these actions are taken with respect to the end-to-end goals of a data flow. In addition to the cognitive aspects of the network, a specification language is needed to translate the user's end-to-end goals into a form understandable by the cognitive process. The cognitive network also depends on a software adaptable network that has both an external interface accessible to the cognitive network and network status sensors. These devices are used to provide control and feedback. The paper concludes by presenting a simple case study to illustrate a cognitive network and its framework

Cognitive networks

TRACKING, PREDICTION, AND SMOOTHING BASICS. g and g-h-k Filters. Kalman Filter. Practical Issues for Radar Tracking. LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAY PROCESSING, AND EXTENDED KALMAN FILTER. Least-Squares and Minimum-Variance Estimates for Linear Time-Invariant Systems. Fixed-Memory Polynomial Filter. Expanding- Memory (Growing-Memory) Polynomial Filters. Fading-Memory (Discounted Least-Squares) Filter. General Form for Linear Time-Invariant System. General Recursive Minimum-Variance Growing-Memory Filter (Bayes and Kalman Filters without Target Process Noise). Voltage Least-Squares Algorithms Revisited. Givens Orthonormal Transformation. Householder Orthonormal Transformation. Gram--Schmidt Orthonormal Transformation. More on Voltage-Processing Techniques. Linear Time-Variant System. Nonlinear Observation Scheme and Dynamic Model (Extended Kalman Filter). Bayes Algorithm with Iterative Differential Correction for Nonlinear Systems. Kalman Filter Revisited. Appendix. Problems. Symbols and Acronyms. Solution to Selected Problems. References. Index.

https://astronu.jinr.ru/wiki/images/8/86/Brookner_Kalman.pdf

Tracking and Kalman Filtering Made Easy

An array antenna system for forming multiple independently steered beams is described. The antenna system includes series or parallel feed circuits and phase shifters which are not disclosed directly in the signal path between the feed circuits and antenna elements included in the array antenna system.

Array antenna having multiple independently steered beams

In recent years phased arrays have seen breakthroughs that lead to capabilities not possible in the past. This is exemplified by the development of GaAs integrated microwave circuits called monolithic microwave integrated circuits (MMIC). This integration has reached the point where it is possible to now build a low cost 35 GHz phased array for a missile seeker costing $30/element (total cost of array including all electronics divided by number of elements). This is made possible here because integration allows the whole T/R module to be put on a single chip. For some application it will be possible in the near future to put multiple receivers or transmitters on a single chip now. The advances provided by Moorepsilas Law has now made it is feasible to do digital beam forming (DBF) with all its numerous advantages. It is now possible to do DBF for a 2,500 element array at the element level, a major breakthrough. Also covered will be: the potential for GaN and SiC chips which have the capability of a factor of ten or more higher peak power than GaAs chips; arrays with instantaneous bandwidths of up to 33:1; SiGe low cost T/R modules; low cost MEMS passive arrays; a real radar application for multiple-input multiple-output (MIMO) as opposed to fantasy has been demonstrated by Lincoln Lab MIT which allows coherent combining of two radars to achieve a 9 dB increase in sensitivity; MIMO also makes possible the optimum removal of clutter in OTH and airborne radars by permitting adaptive control of the transmit antenna pattern in the receiver.

Phased-array and radar astounding breakthroughs — an update

Many think that radar is a mature field, nothing new to happen, it having been around a long time. Nothing can be further from the truth. When I entered the field in the '50s I thought the same thing. The MIT Radiation Lab. Series 28 book volume set summarizing the highly classified World War II work on radar was just published and provided the definitive coverage and there was to be nothing more to learn. How wrong I was. Since then many amazing new developments have taken place. Things are moving even faster now. We live in exciting times. Phased array radars and radars have seen in recent years breakthroughs that lead to capabilities not possible only a few years ago. This is exemplified by the development of GaAs integrated microwave circuits called monolithic microwave integrated circuits (MMIC) which makes it possible to build active electronically scanned arrays (AESAs) having lighter weight, smaller volume, higher reliability and lower cost. MMIC allows the construction of AESAs for applications not feasible before. This integration has reached the point where it is possible to now build a low cost 35 GHz phased array for a missile seeker costing $40/element (total cost of array including all electronics divided by number of elements). The advances provided by Moore's Law has now made it is feasible to do digital beam forming with all its numerous advantages. One advantage of digital beamforming is the ability to lower the search power and occupancy by up to a factor of two. Another advantage is that it makes it possible to achieve the performance of a fully adaptive array without having to do a large matrix inversion, i.e., it makes adaptive-adaptive array processing or equivalently principal decomposition feasible. Also covered will be: the potential for GaN and SiC chips which have the capability of a factor of ten higher peak power than GaAs chips; arrays with instantaneous bandwidths of up to 33:1; SiGe low cost T/R modules; low cost MEMS arrays; metamaterials which provide negative refractivity possibly allowing focusing beyond the diffraction limit; a real radar application for Multiple-Input Multiple-Output (MIMO) as opposed to fantasy has been demonstrated by Lincoln Laboratory MIT which allows the coherent combining of two radars to achieve a 9 dB increase in sensitivity; the ability to build microwave tubes that are smaller, more power efficient, lighter, require lower voltages and have lower cost.

Phased-Array and Radar Breakthroughs

This is a survey paper summarizing the developments and future trends in passive, active bipolar and monolithic microwave integrated circuitry (MMIC) phased array radars for ground, ship, air, and space applications. Covered is the DD(X) ship radar suite; THAAD (formerly GBR); European COBRA; Israel BMD radar antennas; Dutch shipboard APAR; airborne US F-22, JSF and F-18 radars, European AMSAR, Swedish AESA, Japan FSX and Israel Phalcon; Iridium. (66 satellites in orbit for total of 198 antennas) and Globalstar MMIC spaceborne active array systems (these last two are communications but the technology is the same as used by radar systems, in fact the Iridium T/R module technology derives from technology developed for a space based radar); Thales (formerly Thomson-CSF) 4 inch MMIC wafer 94 GHz seeker antenna; digital beamforming; ferroelectric row-column scanning; optical electronic scanning for communications and radar; the MMIC C-band to Ku-band Advanced Shared Aperture Program (ASAP) and AMRFS antenna systems for shared use for communications, radar, electronics countermeasures (ECM) and ESM; and continuous transverse stub (CTS) voltage-variable dielectric (VVD) antenna.

Eli Brookner

Papers

Tracking and Kalman Filtering Made Easy

Array antenna having multiple independently steered beams

Phased-array and radar astounding breakthroughs — an update

Phased-Array and Radar Breakthroughs

Phased array radars-past, present and future