DESCRIPTION ACOUSTICAL LOCATION MONITORING OF A MOBILE TARGET The present invention is concerned with a technique for location monitoring using acoustical signals. The present invention makes use of non-orthogonal processing techniques, the principles of which are described briefly hereinafter by way of background information. Non-orthogonal processing techniques make use of non-orthogonal response characteristics in signal processors. A "non-orthogonal" system is one wherein the responses of processors, e.g. detectors, in a signal domain (e.g. optical wavelength) overlap, as illustrated in Fig. 1 of the accompanying drawings. As evident from Fig. 1, as a result of the overlapping in the signal tails, the outputs of the detectors are cross-correlated, yielding higher sensitivity to signals in the tails. hi principle, the signal processors used in a given non-orthogonal monitoring system will be responsive in a particular signal domain. The signal domain may in principle be any of a plurality of conventional signal domains, including optical, acoustic and radio, each addressed in the frequency (wavelength) or time domains. Additionally, it has been established that other domains such as spatial location, mass, and non-orthogonality between specific parameters (e.g. pressure and temperature etc) plus combinations of large numbers of sensor types can be accommodated. However, the invention described herein is based on the situation where the monitoring signal domain is essentially acoustic and monitoring is achieved by the use of so called chromaticity processing. Chromaticity processing is the name we give to the application of sets of non- orthogonal weighted integrals to signals distributed across a measurement range and the subsequent transformation of the integral quantities obtained to give parameters summarising certain characteristics of the distribution. The name derives from the methods' origins in broadband optics and colour science, where the distribution to which it is applied is that of light intensity across the optical spectrum. However, it is applicable to measurements of any quantity distributed across another variable (for example, acoustic intensity with frequency or temperature with spatial position). Where N integral weightings take the form of Gaussian curves (Fig. 2) the quantities derived in the first part of the process are the values of N basic terms of a Gabor expansion of the original signal. It has also been shown that this process is maximally information preserving for the general signal and that useful information retention with high robustness to noise is obtainable with as few as three Gaussian integrals. hi the optical domain, an approximation to these three Gaussian base integrals is provided by the wavelength response of the sensor elements (e.g. colour photo detectors in CCD cameras). This is known as a tristimulus sensor system. Observations may therefore be represented as data points in a colour space, the most straightforward of which is a Cartesian colour cube having an axis for each of the three sensor elements. The three co-ordinates of a point therefore give a separate measure of each of the familiar red, green and blue components of visible light. Thus, where the original data is a visible spectrum, these axes correspond to the red, green and blue components of a colour and such colour terminology is often applied by analogy where other distribution variables and measurements are involved to aid interpretation. The second stage in chromatic processing (which may, in some circumstances, be omitted, or, where there are only a few discrete values of the distribution variable, be used on its own) is the transformation of the Cartesian colour space into a space referenced by a new set of parameters. These new parameters are formed by the combination of the tristimulus parameters according to the formulae that describe the transformation. Several such transformations are established in colour science, but one in particular has been found to be especially useful for the combination that it makes to operator interpretability of information through its partitioning into components of distinct character. This is the transformation to HLS (Hue, Lightness, Saturation) space. By way of example only, the transformation can be ( 60(G-B) /(max(R,G,B)-min(R,G,B)), if max(R,G,B)=R H = < 60(2+(B-R)) /(max(R,G,B)-min(R,G,B)), ifmax (R,G,B)=G (1) ( 60(4+(R-G)) /(max(R,G,B)-min(R,G,B)), if max (R5G3B)=B

R + G + B L = (2) 3 maxCR . G. BVminfR. G. B) S = (3) max(R, G, B)+min(R, G, B) where R, G and B are the red, green and blue parameters of the Cartesian space, and H, L and S are the hue, lightness and saturation components of the new space. Hue is specified as an angle (given in degrees by the above formula) and the - A - lightness and saturation parameters range from 0 to 1, giving a cylindrical polar space of unit radius and axial extent (Fig. 3). These parameters partition the information acquired such that lightness corresponds to the nominal amplitude of the original measurements summed across the range of their distribution variable, saturation indicates the degree to which the measurements are spread throughout the range of the distribution and hue corresponds to an effective value of the distribution variable about which the measurements are spread. The parameter names reflect the interpretation of these characteristics familiar from colour perception. Where the measurements are of quantities other than visible light, physically analogous and informatically identical (but for some small departure of our colour receptors from a Gaussian response) processing provides an intuitive assimilation of the information represented. Chromaticity monitoring has relied conventionally uponthe non-orthogonality of plural optical detectors for classifying detected signals. In this connection, colour (which is a human perception) may be regarded as a special case of chromaticity, whereas chromaticity may itself be regarded as a special case within the more general area of non-orthogonal signals discrimination. Each detected signal has a special signature which may be classified by N defining parameters, hi general such signatures form highly non-linearly related sets requiring the need for at least N=3 defining parameters for classification in signal space (tri-stmiulus processing). (The use of N=2 parameters (distimulus) constitutes a linear approximation intwo dimensional signal space). The compressed spectral signature may take the form of parameters taken from various signal-defining methodologies such as for instance orthogonal (e.g. Fourier Transformed ) or non-orthogonal (e.g. chromatic) parameters etc. By way of example, if it is assumed that all signals are Gaussian distributions of variable signal strength with respect to the signal domain (e.g. wavelength, frequency, time etc), classes of signals are then unambiguously defined by only N=3 parameters corresponding to (see Fig. 4a):- • Signal amplitude (or power content) (L) • Location of the peak value in signal parameter space (H) • Signal half width (S) If the need for all signals to be Gaussian in nature is relaxed, then each signal may be allocated to one only of a class governed by a mother Gaussian. This provides a substantial but not absolute signal discrimination means through the use of only three detectors (R, G5B,) to yield three functions H,L,S. TMs forms the basis of chromatic discrimination: if the forms of the R5G5B detectors correspond to the responsitivities of the human eye, the N the chromaticity degenerates into the special case of colour. H5L5S are thenN the Hue, Lightness; and Saturation of colour science as described above. Extension of the aforegoing technique to the use of N > 3 parameters leads to a subdivision of each mother Gaussian class into additional non-Gaussian classes (see Fig. 4b). By way of an example, N=4 may define the degree of asymmetric deviation (Skewness) from a Gaussian distribution (see Fig. 4c) i.e.

each Gaussian class NH subdivides into several asymmetric Gaussians x V 1I5 , with x being determined by the signal processor discrimination. Furthermore

an extension to N = 5 parameters enables the degree of Kurtosis of the Gaussian distribution to be determined (see Fig. 4d) leading to a further subdivision of each y asymmetric Gaussian class into ∑ hk subclasses. K=I As mentioned in several places hereinbefore, the signal processors used in a given non-orthogonal monitoring system will be responsive in a particular domain. The aforegoing background discussion has been made on the basis of the optical domain. However, as stated, it has been appreciated that the domain need not in principle be optical and indeed the present invention makes use of measurements made in the spatial acoustic domain. The important point to be noted here is that, for spatially dependant acoustic input signals, exactly the same processing approach and assumptions can be made. Thus, "chromatic processing" can be applied to spatially dependant input signals in the acoustic domain in a precisely analogous manner. Detector signals can therefore still be given the identifying letter R3 G, B leading to chromatic parameters H, L and S, etc.

An object of the present invention is to provide a system and method for identifying the location and/or status of one or more objects or points in space making use of the aforegoing technique. In accordance with a first aspect of the present invention there is provided a system for tracking a mobile target, comprising at least three fixed directional acoustic detectors and an acoustic source as the mobile target, the detectors providing respective electrical output signals which are arranged to be chromatically processed whereby to yield chromatic parameters whose values are dependent upon the target position. Preferably, there is a single acoustic source on the target, although additional sources orientated with respect to each other might be carried on the target. Thus, the measurement domain for the present invention is spatial position addressed via an acoustical, preferably ultrasonic, system. Whereas in the optical domain the chromatic processor has a response which is a function of optical wavelength, inthe present case the chromatic processor, which is acoustical in nature, has a response which is a function of angular position. Thus the approach involves the use of three or more such acoustical ultrasonic processors with non-orthogonal responses in three dimensional space. Preferably, said three fixed, directional acoustic detectors comprise respective microphones disposed in a star arrangement with their axes mutually spaced by 120°. hi some embodiments, the three additional detectors are employed in a delta formation at the periphery of the monitored space, inclined at 120° to each other for enhancing the discrimination ability of the system. Both star and delta arrangements may be deployed together to provide greater enhancement and forming anN=6 chromatic system. Preferably, said three fixed, directional acoustic detectors comprise respective microphones disposed in a delta arrangement at the periphery of a measuring space with their axes mutually spaced by 60° . Preferably, each acoustic source is an ultrasonic source. In some embodiments, a second stage space chromatic processing is performed on said chromatic parameters as a function of time, whereby to generate information as to the movement of the target. In accordance with a second aspect of the present invention there is a system which enables a mobile target to determine its location comprising at least three fixed chromatically directional acoustic sources and an acoustic detector on the mobile target. Each source transmits a coded signal (e.g. frequency, time sequence, etc.) so producing three electrical output signals by the mobile detector which are arranged to be chromatically processed to yield chromatic parameters whose values depend upon the position of the mobile target. It is emphasised that in acoustical techniques described herein, movement can be detected from a known acoustical signal emitted by an object to be tracked, and not by acoustic signals produced by points in the environment. Thus, the object itself is effectively being monitored. The chromatic addressing is undertaken in the spatial domain by processors whose acoustical responses vary with angular position, i.e. non-orthogonality between the angular responses of acoustical detectors each having the same acoustical frequency responsivity. The invention is described further hereinafter, by way of example only, with reference to the accompanying drawings, in which: Fig. 1 illustrates the response of three detectors having overlapping response characteristics; Fig. 2 shows examples of Gaussian curves; Fig. 3 shows H, L and S in cylindrical polar space; Fig. 4a shows how Gaussian signals are unambiguously defined by H, L and S values; Fig. 4b shows how other signals are defined as the Gaussian family to which they belong; Fig. 4c shows how the use of four processors gives a measure of skewness; Fig. 4d shows how the use of five processors gives a measure of kurtosis; Fig. 5 illustrates one embodiment of a non-orthogonal processing system in the acoustic domain in accordance with the present invention; Fig. 6 is a typical H-L plot obtained from the system of Fig. 5, showing the inverse proportionality between source location and L; Fig. 7 is a graph of typical p-p voltage (V) with distance for a detector, showing the relationship between source location and microphone output; Fig. 8 is a typical H-L plot for the system of Fig. 5 showing angular and radial positions of source at different angles, fixed radius (numbers in brackets are measured H, L values); Figs. 9 and 10 show the typical polar responses of three microphones arranged in star and delta formations, respectively; and Fig. 11 illustrates "second generation" processing for the embodiment of Fig. 5. Referring to Fig. 5, there is shown an example in accordance with the present invention of a spatial, non-orthogonal detection system which is based upon a tristimulus acoustic approach and it to be used for the purposes oftracking a target. The embodiment of Fig. 5 uses three directional microphones M1, M2, M L3 (equivalent to N = 3 processors) and a single acoustic source which is arranged to be carried by the mobile target. Thus, the source is itself mobile in that it moves with the target. The directionality of the microphones M1, M2, M3 is arranged to provide the non-orthogonality for space discrimination required to enable the performance of the chromatic processing techniques described. In the embodiment of Fig. 5, the three microphones M1, M2, M3 are clustered together but arranged to be directed at 120° to each others' axis so as to form a star arrangement, as shown in Fig. 9. In an other embodiment shown in Fig. 10, similar microphones M5, M6, M6 can be arranged in a delta configuration. In either case, the source so carried by the moveable target is arranged to emit a single frequency acoustic signal, preferably an ultrasonic signal, which is propagated equally in all directions. The three microphones (detectors) M1, M2, M3 provide respective outputs which are denoted as being Rs Gs, Bs to show their equivalence to the optical processing system described hereinbefore. The outputs of the three microphones (detectors) (Rs Gs, Bs) (Fig. 5(b)) are chromatically processed, for example using the transformations of aforegoing equations (1), (2) and (3) to yield chromatic parameters Hs Ls, Ss (Fig. 5(c)) whose values are dependent upon the position of the source (see Fig. 5(a)). For the above described star arrangement, the value of Hs (still referred to here as the "Hue") yields the angular position, θ, ofthe source S0. The radial position, x, of the source S0from the cluster of detectors M1, M2, M3 is a function of Ls and Hs (Fig. 5(C)). Typical calibration curves for the angular and radial positions of the source S0 in terms of Hs and Ls for a practical system are shown in Figs 6-8. A knowledge of S has the potential to determine the third dimensional location (depth z) of the source S0, the relationship depending upon the angular responsitivity of the detectors (M1, M2, M3) in the third dimension, as well as the inclination of the detectors. Determination of the third dimensional location (depth 2) may be improved by the use of a fourth detector directed perpendicularly to the other three. In the latter case, there would be N = 4 detectors. Several different sources within the detection space can be accommodated simultaneously although each source would be encoded differently. The position of each source would be separately identifiable. More generally the arrangement would form a conglomeration of several N ≥ 3 non-orthogonal acoustic location systems each with 3 < N ≤ 4 detectors and 1 < N ≤ 3 sources whereby the position of each member of the conglomeration within the defined volume would be determined. Further embodiments can utilise 3 ≤ N < 6 in different forms. For example, in one further embodiment, three additional detectors M4, M5, M6 (ie N=6) may be employed in delta formation at the periphery of the detection volume to be monitored and relatively inclined at 120° in the horizontal plane, as shown in Fig. 5(a). Such an embodiment may be used to enhance the discrimination ability of the system, for example with respect to reflections, scattering etc. of the ultrasonic signal from the source by artifacts within the monitored volume. The outputs R0 G0, BD(Fig. 5(d)) from the three additional detectors M4, M5, M6 may for example be processed to yield a further three chromatic parameters HD, SD, L0 (Fig. 5(e)) which may be cross-correlated with Hs, Ss, Ls at the box shown in Fig 5(f). The aforegoing steps constitute a first stage or "first generation" chromatic processing based in the acoustic domain. A second stage or "second generation" chromatic processing based in the spatial domain (e.g. position within a space) may be applied to the first generation, acoustic chromatic co-ordinates . hi this case, the position of the source S0 forms the horizontal axis and the time duration of a chromatic disturbance of the source S0 at a particular position forms the vertical axis (Figure 11 (a)). The time duration/position graph forms a signal graph (Figure 1 Ia) which is addressed by three non-orthogonal chromatic processors (Rp, Gp, Bp) in the position (SPATIAL) domain. Spatial chromatic parameters (e.g. Hp, Gp, Bp) are evaluated from the outputs of Rp, Gp, Bp at various time instants and can be displayed on H13 - Sp, Hp - Lp polar diagrams. Alternatively, and preferably, Hp, Lp, Sp may each be displayed as a function of time (Figure ll(e)). In this manifestation, Hp(t) represents the position of the acoustical source within the monitored volume as a function of time; Lp(t) represents the time duration for which the acoustic source remained located continuously at each location (Figure He). Consequently the movement of the target carrying the source S0 within the monitored space may be tracked via the Hp(t) graph and the stationarity of the target determined from the Lp:t graph. The second generation processing can thus be used in conjunction with the Hp, Lp, Sp to yield quantifiable movement patterns. If the basic delta pattern of detector is used in place of the above described star arrangement, the results are essentially the same in principle, with the angular position θ and radial position again be established by algorithmic manipulation of the H, L, S values. It is possible that alternative systems can be employed in which the source and microphones are interchanged, i.e. the target carries a microphone and there is a star or delta arrangement of acoustic signals. Signal sources can be arranged to be directional and to be encoded, e.g. different frequency or time sequenced, so as to be effectively non-orthogonal and thus enable chromatic processing as described herein to achieve similar results. However, in this case, the information regarding the target positions would be obtained at the target itself and not at the stationary transmitters so that the system would be enabling the target to determine its own location and not for the position to be tracked remotely.